New biomarkers from multiomics approaches: improving risk prediction of atrial fibrillation

Clinical predictive model of new-onset atrial fibrillation in patients with acute myocardial infarction after percutaneous coronary intervention

New-onset atrial fibrillation (NOAF) is associated with increased morbidity and mortality. Despite identifying numerous factors contributing to NOAF, the underlying mechanisms remain uncertain. This study introduces the triglyceride-glucose index (TyG index) as a predictive indicator and establishes a clinical predictive model. We included 551 patients with acute myocardial infarction (AMI) without a history of atrial fibrillation (AF). These patients were divided into two groups based on the occurrence of postoperative NOAF during hospitalization: the NOAF group (n = 94) and the sinus rhythm (SR) group (n = 457). We utilized a regression model to analyze the risk factors of NOAF and to establish a predictive model. The predictive performance, calibration, and clinical effectiveness were evaluated using the receiver operational characteristics (ROC), calibration curve, decision curve analysis, and clinical impact curve. 94 patients developed NOAF during hospitalization. TyG was identified as an independent predictor of NOAF and was significantly higher in the NOAF group. Left atrial (LA) diameter, age, the systemic inflammatory response index (SIRI), and creatinine were also identified as risk factors for NOAF. Combining these with the TyG to build a clinical prediction model resulted in an area under the curve (AUC) of 0.780 (95% CI 0.358-0.888). The ROC, calibration curve, decision curve analysis, and clinical impact curve demonstrated that the performance of the new nomogram was satisfactory. By incorporating the TyG index into the predictive model, NOAF after AMI during hospitalization can be effectively predicted. Early detection of NOAF can significantly improve the prognosis of AMI patients.

Predictive value of NT pro BNP for new-onset atrial fibrillation in heart failure and preserved ejection fraction

AIMS

The prognostic significance of N-terminal pro B-type natriuretic peptide (NT-proBNP) in heart failure with preserved ejection fraction (HFpEF) has been well established. HFpEF and atrial fibrillation (AF) commonly coexist, and each contributes to poor outcomes independently. Nevertheless, the ability of NT-proBNP to predict AF in HFpEF patients remains uncertain.

METHODS AND RESULTS

A total of 367 HFpEF patients without baseline AF from the Treatment of Preserved Cardiac Function Heart Failure with an Aldosterone Antagonist (TOPCAT) trial were included. The Cox proportional hazard model was used to assess the association of NT-proBNP with the risk of AF. The C-statistic, categorical net reclassification index (NRI), and integrated discrimination improvement (IDI) were used to evaluate the ability of NT-proBNP in new-onset AF prediction. During a median follow-up of 2.91 years, 17 (4.63%) new-onset AF cases occurred. Every 1000 pg/mL increase in NT-proBNP was associated with a 16% increase in the risk of AF occurrence after adjustments (hazard ratio, 1.16 [95% CI, 1.02-1.32]). NT-proBNP showed a moderate performance for new-onset AF at 3 years (C-statistic, 0.67). Adding NT-proBNP to CHADS2/R2CHADS2/CHA2DS2-VASc/C2HSET scores improved their predictive performance for AF risk (CHADS2: C-statistic, 0.63, CHADS2+NT: C-statistic, 0.69, NRI, 47.46%, IDI, 1.18%; R2CHADS2: C-statistic, 0.65, R2CHADS2+NT: C-statistic, 0.70, NRI, 48.03%, IDI, 0.51%; CHA2DS2-VASc: C-statistic, 0.67, CHA2DS2-VASc+NT: C-statistic, 0.72, NRI, 49.41%, IDI, 0.86%; C2HSET: C-statistic, 0.77, C2HSET+NT: C-statistic, 0.80, NRI, 50.32%, IDI, 1.58%).

CONCLUSIONS

Among patients with HFpEF, the NT-proBNP level was positively associated with the incidence of new-onset AF and may be a promising predictor.

Diet and risk of atrial fibrillation: a systematic review

Atrial fibrillation (AF) is the most prevalent sustained cardiac arrhythmia. Comprehensive modification of established AF risk factors combined with dietary interventions and breaking deleterious habits has been shown to reduce AF burden and recurrence. Numerous AF risk factors, such as diabetes, obesity or hypertension can be partially related to dietary and lifestyle choices. Therefore, dietary interventions may have potential as a therapeutic approach in AF. Based on available data, current guidelines recommend alcohol abstinence or reduction to decrease AF symptoms, burden, and progression, and do not indicate the need for caffeine abstention to prevent AF episodes (unless it is a trigger for AF symptoms). Uncertainty persists regarding harms or benefits of other dietary factors including chocolate, fish, salt, polyunsaturated and monounsaturated fatty acids, vitamins, and micronutrients. This article provides a systematic review of the association between AF and both dietary patterns and components. Additionally, it discusses potentially related mechanisms and introduces different strategies to assess patients' nutrition patterns, including mobile health solutions and diet indices. Finally, it highlights the gaps in knowledge requiring future investigation.

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.

Plasma Proteomic Insights for Identification of Novel Predictors and Potential Drug Targets in Atrial Fibrillation: A Prospective Cohort Study and Mendelian Randomization Analysis

BACKGROUND

Currently, there are no reliable methods for predicting and preventing atrial fibrillation (AF) in its early stages. This study aimed to identify plasma proteins associated with AF to discover biomarkers and potential drug targets.

METHODS

The UK Biobank Pharma Proteomics Project examined 2923 circulating proteins using the Olink platform, forming the basis of this prospective cohort study. The UK Biobank Pharma Proteomics Project included a randomly selected discovery cohort and the consortium-selected replication cohort. The study's end point was incident AF, identified using International Classification of Diseases, Tenth Revision codes. The association between plasma proteins and incident AF was evaluated using Cox proportional hazard models in both cohorts. Proteins present in both cohorts underwent Mendelian randomization analysis to delineate causal connections, utilizing cis-protein quantitative trait loci as genetic tools. The predictive efficacy of the identified proteins for AF was assessed using the area under the receiver operating characteristic curve, and their druggability was explored.

RESULTS

Data from 38 784 participants were included in this study. Incident AF cases were identified in the discovery cohort (1894; 5.5%) within a median follow-up of 14.5 years and in the replication cohort (451; 10.6%) within a median follow-up of 14.4 years. Twenty-one proteins linked to AF were identified in both cohorts. Specifically, COL4A1 (collagen IV α-1; odds ratio, 1.11 [95% CI, 1.04-1.19]; false discovery rate, 0.016) and RET (proto-oncogene tyrosine-protein kinase receptor Ret; odds ratio, 0.96 [95% CI, 0.94-0.98]; false discovery rate, 0.013) demonstrated a causal link with AF, and RET is druggable. COL4A1 improved the short- and long-term predictive performance of established AF models, as evidenced by significant enhancements in the area under the receiver operating characteristic, integrated discrimination improvement, and net reclassification index, all with P values below 0.05.

CONCLUSIONS

COL4A1 and RET are associated with the development of AF. RET is identified as a potential drug target for AF prevention, while COL4A1 serves as a biomarker for AF prediction. Future studies are needed to evaluate the effectiveness of targeting these proteins to reduce AF risk.

Metabolomics and Biomarkers for Paroxysmal and Persistent Atrial Fibrillation

BACKGROUND

Atrial fibrillation (AF) is the most common type of arrhythmia worldwide and is associated with serious complications. This study investigated the metabolic biomarkers associated with AF and the differences in metabolomics and associated metabolic biomarkers between paroxysmal AF (AFPA) and persistent AF.

METHODS AND RESULTS

Plasma samples were prospectively collected from patients with AF and patients in sinus rhythm with negative coronary angiography. The patients were divided into 3 groups: AFPA, persistent AF, and sinus rhythm (N=54). Metabolomics (n=36) using ultra-high-performance liquid chromatography mass spectrometry was used to detect differential metabolites that were validated in a new cohort (n=18). The validated metabolites from the validation phase were further analyzed by receiver operating characteristic. Among the 36 differential metabolites detected by omics assay, 4 were successfully validated with area under the curve >0.8 (P<0.05). Bioinformatics analysis confirmed the enrichment pathways of unsaturated fatty acid biosynthesis, glyoxylate and dicarboxylate metabolism, and carbon metabolism. Arachidonic acid was a potential biomarker of AFPA, glycolic acid and L-serine were biomarkers of AFPA and persistent AF, and palmitelaidic acid was a biomarker of AFPA.

CONCLUSIONS

In this metabolomics study, we detected 36 differential metabolites in AF, and 4 were validated with high sensitivity and specificity. These differential metabolites are potential biomarkers for diagnosis and monitoring of disease course. This study therefore provides new insights into the precision diagnosis and management of AF.

SingPro: a knowledge base providing single-cell proteomic data

Single-cell proteomics (SCP) has emerged as a powerful tool for detecting cellular heterogeneity, offering unprecedented insights into biological mechanisms that are masked in bulk cell populations. With the rapid advancements in AI-based time trajectory analysis and cell subpopulation identification, there exists a pressing need for a database that not only provides SCP raw data but also explicitly describes experimental details and protein expression profiles. However, no such database has been available yet. In this study, a database, entitled 'SingPro', specializing in single-cell proteomics was thus developed. It was unique in (a) systematically providing the SCP raw data for both mass spectrometry-based and flow cytometry-based studies and (b) explicitly describing experimental detail for SCP study and expression profile of any studied protein. Anticipating a robust interest from the research community, this database is poised to become an invaluable repository for OMICs-based biomedical studies. Access to SingPro is unrestricted and does not mandate a login at: http://idrblab.org/singpro/.

A Predictive Model of New-Onset Atrial Fibrillation After Percutaneous Coronary Intervention in Acute Myocardial Infarction Based on the Lymphocyte to C-Reactive Protein Ratio

Purpose

Lymphocyte to C-reactive protein ratio (LCR) is a recognized systemic inflammatory marker and novel prognostic indicator for several cancers. This study investigated the relationship between preoperative LCR and new-onset atrial fibrillation (NOAF) in patients with acute myocardial infarction (AMI) after percutaneous coronary intervention (PCI).

Patients and Methods

Patients with AMI (n=662) with no history of atrial fibrillation (AF) were enrolled and classified into NOAF and non-NOAF groups based on the occurrence of postoperative NOAF during hospitalization. Logistic regression models were used to analyze NOAF risk factors and to assess the association between preoperative LCR and NOAF incidence. We constructed a new nomogram from the selected NOAF risk factors, and tested its predictive performance, degree of calibration, and clinical utility using receiver operating characteristic and calibration curves, decision curve analysis, and clinical impact curves.

Results

Overall, 84 (12.7%) patients developed NOAF during hospitalization. The LCR was significantly lower in the NOAF group. Preoperative LCR accurately predicted NOAF after AMI and was correlated with increased NOAF risk. Age, body mass index, diabetes, serum albumin levels, uric acid levels, left atrium (LA) diameter, left ventricular ejection fraction, left circumflex artery stenosis > 50%, and Killip class II status were independent predictors of NOAF after AMI. In addition, a new nomogram combined with LCR was constructed to stratify the risk of NOAF in patients with AMI. The performance of the new nomogram was satisfactory, as shown by the receiver operating characteristic curve, calibration curve, decision curve analysis and clinical impact curve.

Conclusion

Preoperative LCR was an independent predictor of NOAF in patients with AMI after PCI. The novel nomogram combined with LCR could rapidly and individually identify and treat patients at a high risk of NOAF.

Identification of a Novel Target Implicated in Chronic Obstructive Sleep Apnea-Related Atrial Fibrillation by Integrative Analysis of Transcriptome and Proteome

Objective

This study aimed to identify a newly identified target involved in atrial fibrillation (AF) linked to chronic obstructive sleep apnea (COSA) through an integrative analysis of transcriptome and proteome.

Methods

Fifteen beagle canines were randomly assigned to three groups: control (CON), obstructive sleep apnea (OSA), and OSA with superior left ganglionated plexi ablation (OSA+GP). A COSA model was established by intermittently obstructing the endotracheal cannula during exhalation for 12 weeks. Left parasternal thoracotomy through the fourth intercostal space allowed for superior left ganglionated plexi (SLGP) ablation. In vivo open-chest electrophysiological programmed stimulation was performed to assess AF inducibility. Histological, transcriptomic, and proteomic analyses were conducted on atrial samples.

Results

After 12 weeks, the OSA group exhibited increased AF inducibility and longer AF durations compared to the CON group. Integrated transcriptomic and proteomic analyses identified 2422 differentially expressed genes (DEGs) and 1194 differentially expressed proteins (DEPs) between OSA and CON groups, as well as between OSA+GP and OSA groups (1850 DEGs and 1418 DEPs). The analysis revealed that differentially regulated DEGs were primarily enriched in mitochondrial biological processes in the CON-vs.-OSA and OSA-vs.-GP comparisons. Notably, the key regulatory molecule GSTZ1 was activated in OSA and inhibited by GP ablation.

Conclusion

These findings suggest that GSTZ1 may play a pivotal role in mitochondrial damage, triggering AF substrate formation, and increasing susceptibility to AF in the context of COSA.

Bayesian linear mixed model with multiple random effects for prediction analysis on high-dimensional multi-omics data

MOTIVATION

Accurate disease risk prediction is an essential step in the modern quest for precision medicine. While high-dimensional multi-omics data have provided unprecedented data resources for prediction studies, their high-dimensionality and complex inter/intra-relationships have posed significant analytical challenges.

RESULTS

We proposed a two-step Bayesian linear mixed model framework (TBLMM) for risk prediction analysis on multi-omics data. TBLMM models the predictive effects from multi-omics data using a hybrid of the sparsity regression and linear mixed model with multiple random effects. It can resemble the shape of the true effect size distributions and accounts for non-linear, including interaction effects, among multi-omics data via kernel fusion. It infers its parameters via a computationally efficient variational Bayes algorithm. Through extensive simulation studies and the prediction analyses on the positron emission tomography imaging outcomes using data obtained from the Alzheimer's Disease Neuroimaging Initiative, we have demonstrated that TBLMM can consistently outperform the existing method in predicting the risk of complex traits.

AVAILABILITY AND IMPLEMENTATION

The corresponding R package is available on GitHub (https://github.com/YaluWen/TBLMM).

An integrated signature of extracellular matrix proteins and a diastolic function imaging parameter predicts post-MI long-term outcomes

Background

Patients suffering from acute myocardial infarction (AMI) are at risk of secondary outcomes including major adverse cardiovascular events (MACE) and heart failure (HF). Comprehensive molecular phenotyping and cardiac imaging during the post-discharge time window may provide cues for risk stratification for the outcomes.

Materials and methods

In a prospective AMI cohort in New Zealand (N = 464), we measured plasma proteins and lipids 30 days after hospital discharge and inferred a unified partial correlation network with echocardiographic variables and established clinical biomarkers (creatinine, c-reactive protein, cardiac troponin I and natriuretic peptides). Using a network-based data integration approach (iOmicsPASS+), we identified predictive signatures of long-term secondary outcomes based on plasma protein, lipid, imaging markers and clinical biomarkers and assessed the prognostic potential in an independent cohort from Singapore (N = 190).

Results

The post-discharge levels of plasma proteins and lipids showed strong correlations within each molecular type, reflecting concerted homeostatic regulation after primary MI events. However, the two molecular types were largely independent with distinct correlation structures with established prognostic imaging parameters and clinical biomarkers. To deal with massively correlated predictive features, we used iOmicsPASS + to identify subnetwork signatures of 211 and 189 data features (nodes) predictive of MACE and HF events, respectively (160 overlapping). The predictive features were primarily imaging parameters, including left ventricular and atrial parameters, tissue Doppler parameters, and proteins involved in extracellular matrix (ECM) organization, cell differentiation, chemotaxis, and inflammation. The network signatures contained plasma protein pairs with area-under-the-curve (AUC) values up to 0.74 for HF prediction in the validation cohort, but the pair of NT-proBNP and fibulin-3 (EFEMP1) was the best predictor (AUC = 0.80). This suggests that there were a handful of plasma proteins with mechanistic and functional roles in predisposing patients to the secondary outcomes, although they may be weaker prognostic markers than natriuretic peptides individually. Among those, the diastolic function parameter (E/e' - an indicator of left ventricular filling pressure) and two ECM proteins, EFEMP1 and follistatin-like 3 (FSTL3) showed comparable performance to NT-proBNP and outperformed left ventricular measures as benchmark prognostic factors for post-MI HF.

Conclusion

Post-discharge levels of E/e', EFEMP1 and FSTL3 are promising complementary markers of secondary adverse outcomes in AMI patients.

The association between eicosanoids and incident atrial fibrillation in the Framingham Heart Study

Chronic inflammation is a continuous low-grade activation of the systemic immune response. Whereas downstream inflammatory markers are associated with atrial fibrillation (AF), upstream inflammatory effectors including eicosanoids are less studied. To examine the association between eicosanoids and incident AF. We used a liquid chromatography-mass spectrometry for the non-targeted measurement of 161 eicosanoids and eicosanoid-related metabolites in the Framingham Heart Study. The association of each eicosanoid and incident AF was assessed using Cox proportional hazards models and adjusted for AF risk factors, including age, sex, height, weight, systolic/diastolic blood pressure, current smoking, antihypertensive medication, diabetes, history of myocardial infarction and heart failure. False discovery rate (FDR) was used to adjust for multiple testing. Eicosanoids with FDR < 0.05 were considered significant. In total, 2676 AF-free individuals (mean age 66 ± 9 years, 56% females) were followed for mean 10.8 ± 3.4 years; 351 participants developed incident AF. Six eicosanoids were associated with incident AF after adjusting for multiple testing (FDR < 0.05). A joint score was built from the top eicosanoids weighted by their effect sizes, which was associated with incident AF (HR = 2.72, CI = 1.71-4.31, P = 2.1 × 10^-5). In conclusion, six eicosanoids were associated with incident AF after adjusting for clinical risk factors for AF.

The Future of Biomarkers in Veterinary Medicine: Emerging Approaches and Associated Challenges

Simple Summary

In this review we seek to outline the role of new technologies in biomarker discovery, particularly within the veterinary field and with an emphasis on ‘omics’, as well as to examine why many biomarkers-despite much excitement-have not yet made it to clinical practice. Further we emphasise the critical need for close collaboration between clinicians, researchers and funding bodies and the need to set clear goals for biomarker requirements and realistic application in the clinical setting, ensuring that biomarker type, method of detection and clinical utility are compatible, and adequate funding, time and sample size are available for all phases of development.

Abstract

New biomarkers promise to transform veterinary practice through rapid diagnosis of diseases, effective monitoring of animal health and improved welfare and production efficiency. However, the road from biomarker discovery to translation is not always straightforward. This review focuses on molecular biomarkers under development in the veterinary field, introduces the emerging technological approaches transforming this space and the role of ‘omics platforms in novel biomarker discovery. The vast majority of veterinary biomarkers are at preliminary stages of development and not yet ready to be deployed into clinical translation. Hence, we examine the major challenges encountered in the process of biomarker development from discovery, through validation and translation to clinical practice, including the hurdles specific to veterinary practice and to each of the ‘omics platforms–transcriptomics, proteomics, lipidomics and metabolomics. Finally, recommendations are made for the planning and execution of biomarker studies with a view to assisting the success of novel biomarkers in reaching their full potential.

Atrial cardiomyopathy markers and new-onset atrial fibrillation risk in patients with acute myocardial infarction

BACKGROUND

New-onset atrial fibrillation (NOAF) after acute myocardial infarction (AMI) is common and independently correlated with poor prognosis. The purpose of this study is to explore whether atrial cardiomyopathy (ACM) markers improve NOAF risk assessment and contribute to therapy decision-making to improve prognosis.

METHODS

We retrospectively analyzed 4713 patients with AMI without a documented history of atrial fibrillation (AF). We measured markers of ACM including P-wave terminal force in ECG lead V1 (PTFV1), Left atrial dimension (LAD), and B-type natriuretic peptide (BNP). Patients were stratified into tertiles of PTFV1, LAD, and BNP levels. Associations between markers and NOAF were evaluated using logistic regression analysis.

RESULTS

Overall, 222 (4.71%) patients had NOAF out of 4713 patients. The prevalence of NOAF increased gradually with PTFV1, LAD, and BNP tertiles. On multivariable regression analysis with potential confounders, elevated PTFV1, LAD, and BNP markers were significantly associated with an increased risk of NOAF. The addition of PTFV1, LAD, and BNP to the AF risk factors recommended by the 2020 ESC Guidelines significantly improved risk discrimination for NOAF.

CONCLUSION

Atrial cardiomyopathy markers including PTFV1, LAD, and BNP were strongly associated with NOAF after AMI. The prediction performance of the clinical model for NOAF was increased by the addition of these markers.

Development and Validation of 3-Year Atrial Fibrillation Prediction Models Using Electronic Health Record With or Without Standardized Electrocardiogram Diagnosis and a Performance Comparison Among Models

Background Improved prediction of atrial fibrillation (AF) may allow for earlier interventions for stroke prevention, as well as mortality and morbidity from other AF-related complications. We developed a clinically feasible and accurate AF prediction model using electronic health records and computerized ECG interpretation. Methods and Results A total of 671 318 patients were screened from 3 tertiary hospitals. After careful exclusion of cases with missing values and a prior AF diagnosis, AF prediction models were developed from the derivation cohort of 25 584 patients without AF at baseline. In the internal/external validation cohort of 117 523 patients, the model using 6 clinical features and 5 ECG diagnoses showed the highest performance for 3-year new-onset AF prediction (C-statistic, 0.796 [95% CI, 0.785-0.806]). A more simplified model using age, sex, and 5 ECG diagnoses (atrioventricular block, fusion beats, marked sinus arrhythmia, supraventricular premature complex, and wide QRS complex) had comparable predictive power (C-statistic, 0.777 [95% CI, 0.766-0.788]). The simplified model showed a similar or better predictive performance than the previous models. In the subgroup analysis, the models performed relatively better in patients without risk factors. Specifically, the predictive power was lower in patients with heart failure or decreased renal function. Conclusions Although the 3-year AF prediction model using both clinical and ECG variables showed the highest performance, the simplified model using age, sex, and 5 ECG diagnoses also had a comparable prediction power with broad applicability for incident AF.