Proteomics and Risk of Atrial Fibrillation in Older Adults (From the Atherosclerosis Risk in Communities [ARIC] Study)

Proteomic Signatures for Risk Prediction of Atrial Fibrillation

BACKGROUND

Proteomic signatures might improve disease prediction and enable targeted disease prevention and management. We explored whether a protein risk score derived from large-scale proteomics data improves risk prediction of atrial fibrillation (AF).

METHODS

A total of 51 680 individuals with 1459 unique plasma protein measurements and without a history of AF were included from the UKB-PPP (UK Biobank Pharma Proteomics Project). A protein risk score was developed with lasso-penalized Cox regression from a random subset of 70% (36 176 individuals, 54.4% women, 2155 events) and was tested on the remaining 30% (15 504 individuals, 54.4% women, 910 events). The protein risk score was externally replicated with the ARIC study (Atherosclerosis Risk in Communities; 11 012 individuals, 54.8% women, 1260 events).

RESULTS

The protein risk score formula developed from the UKB-PPP derivation set was composed of 165 unique plasma proteins, and 15 of them were associated with atrial remodeling. In the UKB-PPP test set, a 1-SD increase in protein risk score was associated with a hazard ratio of 2.20 (95% CI, 2.05-2.41) for incident AF. The C index for a model including CHARGE-AF (Cohorts for Heart and Aging Research in Genomic Epidemiology Atrial Fibrillation), NT-proBNP (N-terminal B-type natriuretic peptide), polygenic risk score, and protein risk score was 0.816 (95% CI, 0.802-0.829) compared with 0.771 (95% CI, 0.755-0.787) for a model including CHARGE-AF, NT-proBNP, and polygenic risk score (C-index change, 0.044 [95% CI, 0.039-0.055]). Protein risk score added to CHARGE-AF, NT-proBNP, and polygenic risk score resulted in a risk reclassification of 5.4% (95% CI, 2.9%-7.9%) with a 5-year risk threshold of 5%. In the decision curve, the predicted net benefit before and after the addition of protein risk score to a model including CHARGE-AF, NT-proBNP, and polygenic risk score was 3.8 and 5.4 per 1000 people, respectively, at a 5-year risk threshold of 5%. External replication of a protein risk score in the ARIC study showed consistent improvement in risk stratification of AF.

CONCLUSIONS

Protein risk score derived from a single plasma sample improved risk prediction of AF. Further research using proteomic signatures in AF screening and prevention is needed.

Deep plasma and tissue proteome profiling of knockout mice reveals pathways associated with Svep1 deficiency

Despite strong causal associations with cardiovascular and metabolic disorders including coronary artery disease, hypertension, and type 2 diabetes, as well as a range of other diseases, the exact function of the protein SVEP1 remains largely unknown. Animal models have been employed to investigate how SVEP1 contributes to disease, with a focus on murine models exploring its role in development, cardiometabolic disease and platelet biology. In this study, we aimed to comprehensively phenotype the proteome of Svep1 +/- mice compared to wild-type (WT) littermates using liquid chromatography-tandem mass spectrometry (LC-MS/MS) bottom-up proteomics in plasma, heart, aorta, lung, and kidney to identify dysregulated pathways and biological functions associated with Svep1 deficiency. Our findings reveal that Svep1 deficiency leads to significant proteomic alterations across the mouse, with the highest number of dysregulated proteins observed in plasma and kidney. Key dysregulated proteins in plasma include upregulation of ADGRV1, CDH1, and MYH6, and downregulation of MTIF2 and AKAP13 which, alongside other proteins dysregulated across tissues, indicate disruption in cell adhesion, extracellular matrix organisation, platelet degranulation, and Rho GTPase pathways. Novel findings include significant enrichment of complement cascades in plasma, suggesting dysregulation of innate immune responses and hemostasis due to Svep1 deficiency. Pathways related to chylomicron assembly and lipid metabolism were also enriched. Additionally, we developed a high-throughput quantitative targeted LC-MS/MS assay to measure endogenous levels of murine SVEP1. SVEP1 was detectable in lung homogenate and showed a significant reduction in SVEP1 levels in Svep1 +/- vs. WT, but was not identified in plasma, heart, aorta, or kidney, likely due to expression levels below the assay's detection limit. Overall, this deep phenotyping study provides insight into the systemic impact of Svep1 deficiency.

2025 Heart Disease and Stroke Statistics: A Report of US and Global Data From the American Heart Association

BACKGROUND

The American Heart Association (AHA), in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, nutrition, sleep, and obesity) and health factors (cholesterol, blood pressure, glucose control, and metabolic syndrome) that contribute to cardiovascular health. The AHA Heart Disease and Stroke Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, brain health, complications of pregnancy, kidney disease, congenital heart disease, rhythm disorders, sudden cardiac arrest, subclinical atherosclerosis, coronary heart disease, cardiomyopathy, heart failure, valvular disease, venous thromboembolism, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs).

METHODS

The AHA, through its Epidemiology and Prevention Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States and globally to provide the most current information available in the annual Statistical Update with review of published literature through the year before writing. The 2025 AHA Statistical Update is the product of a full year's worth of effort in 2024 by dedicated volunteer clinicians and scientists, committed government professionals, and AHA staff members. This year's edition includes a continued focus on health equity across several key domains and enhanced global data that reflect improved methods and incorporation of ≈3000 new data sources since last year's Statistical Update.

RESULTS

Each of the chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics.

CONCLUSIONS

The Statistical Update represents a critical resource for the lay public, policymakers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.

Association of Coagulation Factor XI Level With Cardiovascular Events and Cardiac Function in Community-Dwelling Adults: From ARIC and CHS

BACKGROUND

Coagulation factor XI (FXI) inhibitors are a promising and novel class of anticoagulants, but a recent animal study found that FXI inhibition exacerbated diastolic dysfunction and heart failure (HF). In the ARIC study (Atherosclerosis Risk in Communities), we investigated whether plasma FXI level was associated with cardiovascular events and cardiac function.

METHODS

ARIC was our primary analytic cohort. We included 4471 participants (median age, 75 years; 57% female; 17% Black) who attended visit 5 (2011-2013) with Somalogic-quantified plasma FXI levels and echocardiographic cardiac function. Prevalent HF and atrial fibrillation (AF) cases were defined as having HF or AF diagnosed at or before each participant's visit 5 exam date. Incident HF and AF events were ascertained through 2021. Associations were assessed using Cox, logistic, and linear regression models. Primary prospective associations were also validated in the CHS (Cardiovascular Health Study) using an orthogonal FXI assay (enzyme-linked immunosorbent assay).

RESULTS

At ARIC visit 5, there were 665 and 419 participants with prevalent HF and AF, respectively. During a median follow-up of 9 years, there were 580 and 788 incident HF and AF events, respectively. Lower FXI level was associated prospectively with higher incidence of HF (hazard ratio [HR], 1.36 [for each 1-unit decrement of log2-transformed FXI level] [95% CI, 1.01-1.83]) but not incident AF, and cross-sectionally with increased odds of AF (odds ratio [OR], 1.96 [95% CI, 1.23-3.07]) but not HF. In age-stratified analyses, decreased FXI was associated with higher incidence of HF in participants ≥75 years of age (HR, 1.57 [95% CI, 1.08-2.28]) but not <75 years of age (HR, 1.11 [95% CI, 0.68-1.79]). The inverse FXI-HF association was validated in CHS (HR, 1.18 [95% CI, 1.02-1.36]). At ARIC visit 5, lower FXI level was also associated with higher prevalence of diastolic dysfunction and worse E/A ratio, left atrial (LA) volume index, LA function, and left ventricular mass index, but not left ventricular ejection fraction or global longitudinal strain.

CONCLUSIONS

Decreased FXI level is associated with greater incidence of HF, especially in older adults. It is also associated with prevalent AF, worse diastolic function, worse LA function, and greater LA size. More research is needed to assess potential unwanted effects of FXI inhibition on the risk of cardiovascular events and cardiac function.

Reduced PI3K(p110α) induces atrial myopathy, and PI3K-related lipids are dysregulated in athletes with atrial fibrillation

BACKGROUND

Elucidating mechanisms underlying atrial myopathy, which predisposes individuals to atrial fibrillation (AF), will be critical for preventing/treating AF. In a serendipitous discovery, we identified atrial enlargement, fibrosis, and thrombi in mice with reduced phosphoinositide 3-kinase (PI3K) in cardiomyocytes. PI3K(p110α) is elevated in the heart with exercise and is critical for exercise-induced ventricular enlargement and protection, but the role in the atria was unknown. Physical inactivity and extreme endurance exercise can increase AF risk. Therefore, our objective was to investigate whether too little and/or too much PI3K alone induces cardiac pathology.

METHODS

New cardiomyocyte-specific transgenic mice with increased or decreased PI3K(p110α) activity were generated. Multi-omics was conducted in mouse atrial tissue, and lipidomics in human plasma.

RESULTS

Elevated PI3K led to an increase in heart size with preserved/enhanced function. Reduced PI3K led to atrial dysfunction, fibrosis, arrhythmia, increased susceptibility to atrial enlargement and thrombi, and dysregulation of monosialodihexosylganglioside (GM3), a lipid that regulates insulin-like growth factor-1 (IGF1)-PI3K signaling. Proteomic profiling identified distinct signatures and signaling networks across atria with varying degrees of dysfunction, enlargement, and thrombi, including commonalities with the human AF proteome. PI3K-related lipids were dysregulated in plasma from athletes with AF.

CONCLUSION

PI3K(p110α) is a critical regulator of atrial biology and function in mice. This work provides a proteomic resource of candidates for further validation as potential new drug targets and biomarkers for atrial myopathy. Further investigation of PI3K-related lipids as markers for identifying individuals at risk of AF is warranted. Dysregulation of PI3K may contribute to the association between increased cardiac risk with physical inactivity and extreme endurance exercise.

Multimodal data integration to predict atrial fibrillation

Aims

Many studies have utilized data sources such as clinical variables, polygenic risk scores, electrocardiogram (ECG), and plasma proteins to predict the risk of atrial fibrillation (AF). However, few studies have integrated all four sources from a single study to comprehensively assess AF prediction.

Methods and results

We included 8374 (Visit 3, 1993-95) and 3730 (Visit 5, 2011-13) participants from the Atherosclerosis Risk in Communities Study to predict incident AF and prevalent (but covert) AF. We constructed a (i) clinical risk score using CHARGE-AF clinical variables, (ii) polygenic risk score using pre-determined weights, (iii) protein risk score using regularized logistic regression, and (iv) ECG risk score from a convolutional neural network. Risk prediction performance was measured using regularized logistic regression. After a median follow-up of 15.1 years, 1910 AF events occurred since Visit 3 and 229 participants had prevalent AF at Visit 5. The area under curve (AUC) improved from 0.660 to 0.752 (95% CI, 0.741-0.763) and from 0.737 to 0.854 (95% CI, 0.828-0.880) after addition of the polygenic risk score to the CHARGE-AF clinical variables for predicting incident and prevalent AF, respectively. Further addition of ECG and protein risk scores improved the AUC to 0.763 (95% CI, 0.753-0.772) and 0.875 (95% CI, 0.851-0.899) for predicting incident and prevalent AF, respectively.

Conclusion

A combination of clinical and polygenic risk scores was the most effective and parsimonious approach to predicting AF. Further addition of an ECG risk score or protein risk score provided only modest incremental improvement for predicting AF.

Assessment of Inflammatory Biomarkers and Incident Atrial Fibrillation in Older Adults

BACKGROUND

Available evidence supports the importance of inflammation in atrial fibrillation (AF) pathogenesis, yet general anti-inflammatory therapies have failed to show benefit for prevention of the arrhythmia. Better understanding of the specific inflammatory pathways involved is necessary to advance therapeutics.

METHODS AND RESULTS

We evaluated 9 circulating markers of inflammation measured by immunoassays and incidence of AF in a population-based older cohort. Biomarkers included measures of general inflammation and the NLR (nucleotide-binding oligomerization domain-like receptor) family pyrin domain containing 3 inflammasome, TNF-α (tumor necrosis factor α), monocyte activation markers, and sIL-2 (soluble interleukin-2). Among 5726 participants (median age 72 years), 1836 developed AF over median follow-up of 11.5 years. After adjustment for conventional risk factors, 5 biomarkers were positively associated with incident AF: IL-6 (interleukin-6), hazard ratio (HR), 1.14 (95% CI, 1.07-1.21); hs-CRP (high-sensitivity C-reactive protein), HR, 1.05 (95% CI, 1.01-1.09); white blood cell count, HR, 1.18 (95% CI, 1.04-1.35); sTNFR1 (soluble TNF receptor 1), HR, 1.21 (95% CI, 1.05-1.39); and sIL-2Rα (sIL-2 receptor α), HR, 1.16 (95% CI, 1.05-1.29) (all per doubling of biomarker). sCD14, sCD163, IL-18, and IL-1 receptor antagonist showed no association with AF. Upon concurrent adjustment for all biomarkers, only IL-6 remained significantly associated with the arrhythmia, HR, 1.17 (95% CI, 1.07-1.26).

CONCLUSIONS

Among older adults, IL-6, hs-CRP, white blood cell count, sTNFR1, and sIL-2Rα were positively associated with incident AF, but only IL-6 retained significance on concurrent adjustment. These findings newly document associations for sTNFR1 and sIL-2Rα and lend support to a preeminent role for IL-6 in development of this arrhythmia. The efficacy of IL-6 blockade for AF prevention awaits completion of appropriate clinical trials.

Protein Biomarkers of Adverse Clinical Features and Events in Sarcomeric Hypertrophic Cardiomyopathy

BACKGROUND

Hypertrophic cardiomyopathy (HCM) is a heterogeneous condition that can lead to atrial fibrillation, heart failure, and sudden cardiac death in many individuals but mild clinical impact in others. The mechanisms underlying this phenotypic heterogeneity are not well defined. The aim of this study was to use plasma proteomic profiling to help illuminate biomarkers that reflect or inform the heterogeneity observed in HCM.

METHODS

The Olink antibody-based proteomic platform was used to measure plasma proteins in patients with genotype positive (sarcomeric) HCM participating in the HCM Registry. We assessed associations between plasma protein levels with clinical features, cardiac magnetic resonance imaging metrics, and the development of atrial fibrillation.

RESULTS

We measured 275 proteins in 701 patients with sarcomeric HCM. There were associations between late gadolinium enhancement with proteins reflecting neurohormonal activation (NT-proBNP [N-terminal pro-B-type natriuretic peptide] and ACE2 [angiotensin-converting enzyme 2]). Metrics of left ventricular remodeling had novel associations with proteins involved in vascular development and homeostasis (vascular endothelial growth factor-D and TM [thrombomodulin]). Assessing clinical features, the European Society of Cardiology sudden cardiac death risk score was inversely associated with SCF (stem cell factor). Incident atrial fibrillation was associated with mediators of inflammation and fibrosis (MMP2 [matrix metalloproteinase 2] and SPON1 [spondin 1]).

CONCLUSIONS

Proteomic profiling of sarcomeric HCM identified biomarkers associated with adverse imaging and clinical phenotypes. These circulating proteins are part of both established pathways, including neurohormonal activation and fibrosis, and less familiar pathways, including endothelial function and inflammatory proteins less well characterized in HCM. These findings highlight the value of plasma profiling to identify biomarkers of risk and to gain further insights into the pathophysiology of HCM.

Enhanced prediction of atrial fibrillation risk using proteomic markers: a comparative analysis with clinical and polygenic risk scores

BACKGROUND

Proteomic biomarkers have shown promise in predicting various cardiovascular conditions, but their utility in assessing the risk of atrial fibrillation (AF) remains unclear. This study aimed to develop and validate a protein-based risk score for predicting incident AF and to compare its predictive performance with traditional clinical risk factors and polygenic risk scores in a large cohort from the UK Biobank.

METHODS

We analysed data from 36 129 white British individuals without prior AF, assessing 2923 plasma proteins using the Olink Explore 3072 assay. The cohort was divided into a training set (70%) and a test set (30%) to develop and validate a protein risk score for AF. We compared the predictive performance of this score with the HARMS2-AF risk model and a polygenic risk score.

RESULTS

Over an average follow-up of 11.8 years, 2450 incident AF cases were identified. A 47-protein risk score was developed, with N-terminal prohormone of brain natriuretic peptide (NT-proBNP) being the most significant predictor. In the test set, the protein risk score (per SD increment, HR 1.94; 95% CI 1.83 to 2.05) and NT-proBNP alone (HR 1.80; 95% CI 1.70 to 1.91) demonstrated superior predictive performance (C-statistic: 0.802 and 0.785, respectively) compared with HARMS2-AF and polygenic risk scores (C-statistic: 0.751 and 0.748, respectively).

CONCLUSIONS

A protein-based risk score, particularly incorporating NT-proBNP, offers superior predictive value for AF risk over traditional clinical and polygenic risk scores, highlighting the potential for proteomic data in AF risk stratification.

Proteomic aging clock (PAC) predicts age-related outcomes in middle-aged and older adults

Beyond mere prognostication, optimal biomarkers of aging provide insights into qualitative and quantitative features of biological aging and might, therefore, offer useful information for the testing and, ultimately, clinical use of gerotherapeutics. We aimed to develop a proteomic aging clock (PAC) for all-cause mortality risk as a proxy of biological age. Data were from the UK Biobank Pharma Proteomics Project, including 53,021 participants aged between 39 and 70 years and 2923 plasma proteins assessed using the Olink Explore 3072 assay®. 10.9% of the participants died during a mean follow-up of 13.3 years, with the mean age at death of 70.1 years. The Spearman correlation between PAC proteomic age and chronological age was 0.77. PAC showed robust age-adjusted associations and predictions for all-cause mortality and the onset of various diseases in general and disease-free participants. The proteins associated with PAC proteomic age deviation were enriched in several processes related to the hallmarks of biological aging. Our results expand previous findings by showing that biological age acceleration, based on PAC, strongly predicts all-cause mortality and several incident disease outcomes. Particularly, it facilitates the evaluation of risk for multiple conditions in a disease-free population, thereby, contributing to the prevention of initial diseases, which vary among individuals and may subsequently lead to additional comorbidities.

Transcriptomic Analysis of Extracellular Vesicles in the Search for Novel Plasma and Thrombus Biomarkers of Ischemic Stroke Etiologies

Accurate etiologic diagnosis provides an appropriate secondary prevention and better prognosis in ischemic stroke (IS) patients; still, 45% of IS are cryptogenic, urging us to enhance diagnostic precision. We have studied the transcriptomic content of plasma extracellular vesicles (EVs) (n = 21) to identify potential biomarkers of IS etiologies. The proteins encoded by the selected genes were measured in the sera of IS patients (n = 114) and in hypertensive patients with (n = 78) and without atrial fibrillation (AF) (n = 20). IGFBP-2, the most promising candidate, was studied using immunohistochemistry in the IS thrombi (n = 23) and atrium of AF patients (n = 13). In vitro, the IGFBP-2 blockade was analyzed using thromboelastometry and endothelial cell cultures. We identified 745 differentially expressed genes among EVs of cardioembolic, atherothrombotic, and ESUS groups. From these, IGFBP-2 (cutoff > 247.6 ng/mL) emerged as a potential circulating biomarker of embolic IS [OR = 8.70 (1.84-41.13) p = 0.003], which was increased in patients with AF vs. controls (p < 0.001) and was augmented in cardioembolic vs. atherothrombotic thrombi (p < 0.01). Ex vivo, the blockage of IGFBP-2 reduced clot firmness (p < 0.01) and lysis time (p < 0.001) and in vitro, diminished endothelial permeability (p < 0.05) and transmigration (p = 0.06). IGFBP-2 could be a biomarker of embolic IS and a new therapeutic target involved in clot formation and endothelial dysfunction.

2024 Heart Disease and Stroke Statistics: A Report of US and Global Data From the American Heart Association

BACKGROUND

The American Heart Association (AHA), in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, nutrition, sleep, and obesity) and health factors (cholesterol, blood pressure, glucose control, and metabolic syndrome) that contribute to cardiovascular health. The AHA Heart Disease and Stroke Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, brain health, complications of pregnancy, kidney disease, congenital heart disease, rhythm disorders, sudden cardiac arrest, subclinical atherosclerosis, coronary heart disease, cardiomyopathy, heart failure, valvular disease, venous thromboembolism, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs).

METHODS

The AHA, through its Epidemiology and Prevention Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States and globally to provide the most current information available in the annual Statistical Update with review of published literature through the year before writing. The 2024 AHA Statistical Update is the product of a full year's worth of effort in 2023 by dedicated volunteer clinicians and scientists, committed government professionals, and AHA staff members. The AHA strives to further understand and help heal health problems inflicted by structural racism, a public health crisis that can significantly damage physical and mental health and perpetuate disparities in access to health care, education, income, housing, and several other factors vital to healthy lives. This year's edition includes additional global data, as well as data on the monitoring and benefits of cardiovascular health in the population, with an enhanced focus on health equity across several key domains.

RESULTS

Each of the chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics.

CONCLUSIONS

The Statistical Update represents a critical resource for the lay public, policymakers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.

A proteomic analysis of atrial fibrillation in a prospective longitudinal cohort (AGES-Reykjavik study)

AIMS

Atrial fibrillation (AF) is associated with high risk of comorbidities and mortality. Our aim was to examine causal and predictive relationships between 4137 serum proteins and incident AF in the prospective population-based Age, Gene/Environment Susceptibility-Reykjavik (AGES-Reykjavik) study.

METHODS AND RESULTS

The study included 4765 participants, of whom 1172 developed AF. Cox proportional hazards regression models were fitted for 4137 baseline protein measurements adjusting for known risk factors. Protein associations were tested for replication in the Cardiovascular Health Study (CHS). Causal relationships were examined in a bidirectional, two-sample Mendelian randomization analysis. The time-dependent area under the receiver operating characteristic curve (AUC)-statistic was examined as protein levels and an AF-polygenic risk score (PRS) were added to clinical risk models. The proteomic signature of incident AF consisted of 76 proteins, of which 63 (83%) were novel and 29 (38%) were replicated in CHS. The signature included both N-terminal prohormone of brain natriuretic peptide (NT-proBNP)-dependent (e.g. CHST15, ATP1B1, and SVEP1) and independent components (e.g. ASPN, AKR1B, and LAMA1/LAMB1/LAMC1). Nine causal candidates were identified (TAGLN, WARS, CHST15, CHMP3, COL15A1, DUSP13, MANBA, QSOX2, and SRL). The reverse causal analysis suggested that most AF-associated proteins were affected by the genetic liability to AF. N-terminal prohormone of brain natriuretic peptide improved the prediction of incident AF events close to baseline with further improvements gained by the AF-PRS at all time points.

CONCLUSION

The AF proteomic signature includes biologically relevant proteins, some of which may be causal. It mainly reflects an NT-proBNP-dependent consequence of the genetic liability to AF. N-terminal prohormone of brain natriuretic peptide is a promising marker for incident AF in the short term, but risk assessment incorporating a PRS may improve long-term risk assessment.

The emerging Janus face of SVEP1 in development and disease

Sushi, von Willebrand factor type A, EGF, and pentraxin domain containing 1 (SVEP1) is a large extracellular matrix protein that is also detected in circulation. Recent plasma proteomic and genomic studies have revealed a large number of associations between SVEP1 and human traits, particularly chronic disease. These include associations with cardiac death and disease, diabetes, platelet traits, glaucoma, dementia, and aging; many of these are causal. Animal models demonstrate that SVEP1 is critical in vascular development and disease, but its molecular and cellular mechanisms remain poorly defined. Future studies should aim to characterize these mechanisms and determine the diagnostic, prognostic, and therapeutic value of measuring or intervening on this enigmatic protein.

Growth Differentiation Factor 15 and Risk of Bleeding Events: The Atherosclerosis Risk in Communities Study

Background GDF15 (growth differentiation factor 15) is a potent predictor of bleeding in people with cardiovascular disease. However, whether GDF15 is associated with bleeding in individuals without a history of cardiovascular disease is unknown. Methods and Results The study population was from the ARIC (Atherosclerosis Risk in Communities) study. We studied the association of GDF15 with hospitalized bleeding events among 9205 participants (1993-1995) without prior bleeding and cardiovascular disease (mean age 60 years, 57% women, 21% Black). Plasma levels of GDF15 were measured in relative fluorescence units using DNA-based aptamer technology. Bleeding was ascertained using discharge codes. We examined hazard ratios (HRs) of incident bleeding using Cox models and risk prediction with the addition of GDF15 to clinical predictors of bleeding. There were 1328 hospitalizations with bleeding during a median follow-up of 22.5 years. The majority (76.5%) were because of gastrointestinal bleeding. The absolute incidence rate of bleeding per 1000 person-years was 11.64 in the highest quartile of GDF15 versus 5.22 in the lowest quartile. The highest versus lowest quartile of GDF15 demonstrated an adjusted HR of 2.00 (95% CI, 1.69-2.35) for total bleeding. The findings were consistent when we examined bleeding as the primary discharge diagnosis. The addition of GDF15 to clinical predictors of bleeding improved the C-statistic by 0.006 (0.002-0.011) from 0.684 to 0.690, P=0.008. Conclusions Higher levels of GDF15 were associated with bleeding events and improved the risk prediction beyond clinical predictors in individuals without cardiovascular disease.

Heart Disease and Stroke Statistics-2023 Update: A Report From the American Heart Association

BACKGROUND

The American Heart Association, in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, diet, and weight) and health factors (cholesterol, blood pressure, and glucose control) that contribute to cardiovascular health. The Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, congenital heart disease, rhythm disorders, subclinical atherosclerosis, coronary heart disease, heart failure, valvular disease, venous disease, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs).

METHODS

The American Heart Association, through its Epidemiology and Prevention Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States to provide the most current information available in the annual Statistical Update with review of published literature through the year before writing. The 2023 Statistical Update is the product of a full year's worth of effort in 2022 by dedicated volunteer clinicians and scientists, committed government professionals, and American Heart Association staff members. The American Heart Association strives to further understand and help heal health problems inflicted by structural racism, a public health crisis that can significantly damage physical and mental health and perpetuate disparities in access to health care, education, income, housing, and several other factors vital to healthy lives. This year's edition includes additional COVID-19 (coronavirus disease 2019) publications, as well as data on the monitoring and benefits of cardiovascular health in the population, with an enhanced focus on health equity across several key domains.

RESULTS

Each of the chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics.

CONCLUSIONS

The Statistical Update represents a critical resource for the lay public, policymakers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.

Analytical Considerations of Large-Scale Aptamer-Based Datasets for Translational Applications

The development and advancement of aptamer technology has opened a new realm of possibilities for unlocking the biocomplexity available within proteomics. With ultra-high-throughput and multiplexing, alongside remarkable specificity and sensitivity, aptamers could represent a powerful tool in disease-specific research, such as supporting the discovery and validation of clinically relevant biomarkers. One of the fundamental challenges underlying past and current proteomic technology has been the difficulty of translating proteomic datasets into standards of practice. Aptamers provide the capacity to generate single panels that span over 7000 different proteins from a singular sample. However, as a recent technology, they also present unique challenges, as the field of translational aptamer-based proteomics still lacks a standardizing methodology for analyzing these large datasets and the novel considerations that must be made in response to the differentiation amongst current proteomic platforms and aptamers. We address these analytical considerations with respect to surveying initial data, deploying proper statistical methodologies to identify differential protein expressions, and applying datasets to discover multimarker and pathway-level findings. Additionally, we present aptamer datasets within the multi-omics landscape by exploring the intersectionality of aptamer-based proteomics amongst genomics, transcriptomics, and metabolomics, alongside pre-existing proteomic platforms. Understanding the broader applications of aptamer datasets will substantially enhance current efforts to generate translatable findings for the clinic.