Background/Objectives: Carfilzomib (CFZ) and bortezomib (BTZ) are proteasome inhibitors used as the first-line therapy for relapsed or refractory multiple myeloma (MM) but are associated with cardiovascular adverse events (CVAEs). This study aims to identify differentially methylated positions (DMPs) and regions (DMRs), and enriched pathways associated with CVAEs related to CFZ or BTZ-based treatment. Methods: Baseline germline DNA methylation profiles from 79 MM patients (49 on CFZ and 30 on BTZ) in the Prospective Study of Cardiac Events During Proteasome Inhibitor Therapy (PROTECT) were analyzed. Epigenome-wide analyses were performed within each group, followed by meta-analyses to identify signals common to CVAEs associated with both medicines. Results: Four DMPs were significantly associated with CFZ-CVAEs, including cg15144237 within ENSG00000224400 (p = 9.45 × 10-10), cg00927646 within TBX3 (p = 9.78 × 10-8), and cg10965131 within WDR86 (p = 1.00 × 10-7). One DMR was identified in the FAM166B region (p = 5.46 × 10-7). There was no evidence of any DMPs in BTZ-treated patients, however two DMPs and one DMR reached a suggestive level of significance (p < 1.00 × 10-5): cg09666417 in DNAJC18 (p = 3.41 × 10-7) and cg12987761 in USP18 (p = 5.00 × 10-7), and a DMR mapped to the WDR86/WDR86-AS1 region (p = 8.11 × 10-8). Meta-analysis did not find any significant DMPs, with the top CpG being cg17933807 in GNL2 (p = 7.38 × 10-5). Pathway enrichment analyses identified peroxisome, MAPK, Rap1, adherens junction, phospholipase D, autophagy, and aldosterone-related pathways to be implicated in CVAEs. Conclusions: Our study identified distinct DMPs, DMRs, and pathways enrichment associated with CVAE, suggesting epigenetic contributors to CVAEs and supporting the need for larger validation studies.

OBJECTIVE:Our objective was to build classifiers for multiple phenotypes that categorize a cohort of adults with congenital heart disease (ACHD), that can be used to populate variables in a biobank. MATERIALS AND METHODS:A dataset of 1492 ACHD patients, with expert-created labels for eight phenotypes, was created and used to train classifiers with three different architectures. A larger unlabeled dataset containing 15,869 patients was used to pre-train the classifiers, and a 20 % subset of the unlabeled dataset was used to validate the classifier predictions. RESULTS:On held out labeled data, F1 scores for the eight target phenotypes of interest ranged from 0.66 to 1. Of those, the six phenotypes with best classification performance were then validated on unlabeled data, where positive predictive value ranged from 81.5 % to 100 %. DISCUSSION:We were able to classify six out of eight phenotypes with satisfactory performance. Challenging phenotypes included cyanosis and New York Heart Association functional class. Both vary over time and in the latter case there is limited agreement between human observers. Different phenotypes benefited from different model architectures to some degree, but the differences are small enough that uniformity of deployment may be a more important factor in choosing what models to deploy. We saw no benefit to joint training, but some phenotypes benefited from a multiclass model. CONCLUSION:Human-curated data can be used to train text-based ACHD phenotype classifiers with promising internal performance acceptable for application in quality improvement efforts and to populate ACHD registry data.

Heart failure (HF) affects 6.7 million people in the US and includes two major subtypes, HF with reduced ejection fraction (HFrEF) and HF with preserved ejection fraction (HFpEF), with distinct genetic architectures. We meta-analyze genome-wide association studies (GWAS) of 38,781 HFrEF cases, 38,163 HFpEF cases, and 526,135 controls across European, African, Hispanic, and Asian ancestries using the Million Veteran Program and Vanderbilt University DNA Databank (BioVU). We identify 46 genome-wide significant loci for HFrEF (9 novel) and 3 loci for HFpEF (1 novel). Four HFrEF loci are detected in African ancestry participants near CD36, SPI1, TRIM48, and SPNS3, with lead SNPs showing low risk-allele frequencies in European populations. In the all-cause HF meta-analysis (200,070 cases, 2,076,466 controls), we identify 136 loci (12 novel). Gene-based tests, tissue enrichment, transcriptome-wide association, and fine-mapping implicate vascular, metabolic, and TGF-β/Smad signaling pathways and nominate candidate causal genes, clarifying shared and subtype-specific risk across ancestries. This study maps the genetic basis of major heart failure subtypes across diverse populations, identifying shared and subtype-specific risk variants that may inform biology, risk prediction and future therapies.

Heart failure with preserved ejection fraction (HFpEF) is an increasingly common cause of morbidity and mortality in older adults that is driven by cardiac and non-cardiac mechanisms. Physical rehabilitation improves frailty and functional capacity in HFpEF, though underlying mechanisms remain less clear. We quantified >5,000 circulating proteins across two randomized clinical trials of rehabilitation in HFpEF (REHAB-HF, SECRET-II), identifying proteins associated with prognostic measures of physical function (short physical performance battery, 6-minute walk distance) and protein changes after rehabilitation. Using an artificial intelligence (AI)-enabled multiplex network analysis (MENTOR-IA), we identified biologically plausible networks central to this "physical function proteome," including endothelial remodeling, mitochondrial metabolism, calcium handling, and immune modulation. Expression of prioritized proteins at the transcriptional level localized to heart, skeletal muscle, and brain tissue, with several cognate transcripts implicated in frailty via tissue-specific transcriptome-wide genetic association studies. In addition, using novel human genetic approaches, we implicated select proteins as mediating tissue-specific genetic effects on frailty. These findings motivated us to construct multi-protein signatures of physical function, which correlated with functional changes observed with rehabilitation in REHAB-HF and SECRET-II and that were associated with heart failure and multi-dimensional clinical outcomes in >26,000 individuals. These findings collectively delineate a multi-system molecular program underlying physical function impairment and rehabilitation response in HFpEF, offering insights into potential precision risk estimators and therapeutic targets for surveillance and promotion of physiologic resilience.

ABSTRACT Background Atrial Fibrillation (AF) is a common and clinically heterogeneous arrythmia. Machine learning (ML) algorithms can define data-driven disease subtypes in an unbiased fashion, but whether the AF subgroups defined in this way align with underlying mechanisms, such as high polygenic liability to AF or inflammation, and associate with clinical outcomes is unclear. Methods We identified individuals with AF in a large biobank linked to electronic health records (EHR) and genome-wide genotyping. The phenotypic architecture in the AF cohort was defined using principal component analysis of 35 expertly curated and uncorrelated clinical features. We applied an unsupervised co-clustering machine learning algorithm to the 35 features to identify distinct phenotypic AF clusters. The clinical inflammatory status of the clusters was defined using measured biomarkers (CRP, ESR, WBC, Neutrophil %, Platelet count, RDW) within 6 months of first AF mention in the EHR. Polygenic risk scores (PRS) for AF and cytokine levels were used to assess genetic liability of clusters to AF and inflammation, respectively. Clinical outcomes were collected from EHR up to the last medical contact. Results The analysis included 23,271 subjects with AF, of which 6,023 had available genome-wide genotyping. The machine learning algorithm identified 3 phenotypic clusters that were distinguished by increasing prevalence of comorbidities, particularly renal dysfunction, and coronary artery disease. Polygenic liability to AF across clusters was highest in the low comorbidity cluster. Clinically measured inflammatory biomarkers were highest in the high comorbid cluster, while there was no difference between groups in genetically predicted levels of inflammatory biomarkers. Subgroup assignment was associated with multiple clinical outcomes including mortality, stroke, bleeding, and use of cardiac implantable electronic devices after AF diagnosis. Conclusion Patient subgroups identified by unsupervised clustering were distinguished by comorbidity burden and associated with risk of clinically important outcomes. Polygenic liability to AF across clusters was greatest in the low comorbidity subgroup. Clinical inflammation, as reflected by measured biomarkers, was lowest in the subgroup with lowest comorbidities. However, there were no differences in genetically predicted levels of inflammatory biomarkers, suggesting associations between AF and inflammation is driven by acquired comorbidities rather than genetic predisposition.

Carfilzomib is highly effective in the treatment of multiple myeloma, but it has been associated with cardiovascular adverse events that impact patient outcomes. Our prior global metabolomic analyses indicated an association between hydrophilic bile acids and carfilzomib-cardiotoxicity risk, although a causal relationship remained to be determined. Here, our objective was to validate the previously identified bile acids in an independent cohort and investigate whether these hydrophilic bile acids play a causal role in carfilzomib-cardiotoxicity using Mendelian randomization. Using targeted metabolomics, we validated the association between glycoursodeoxycholic acid and carfilzomib-cardiotoxicity in an independent cohort (n = 61). We then performed two-sample Mendelian randomization analyses, using metabolome-wide association study results to provide the genetic instruments for bile acid levels as exposure and genome-wide association study summary statistics from the UK Biobank (n = 484,598) as the outcome data for cardiovascular adverse events. Causal inference was assessed with the inverse-variance weighted method, followed by multiple sensitivity analyses. Higher glycoursodeoxycholic acid concentration (odds ratio = 0.34, P = 0.032) was associated with lower cardiotoxicity risk after adjusting for hypertension and high levels of brain natriuretic peptides. Mendelian randomization analysis demonstrated a robust causal effect of glycoursodeoxycholic acid on cardiotoxicity risk (β = -0.00065, P = 6.2 × 10-5). Gene enrichment analysis indicated pathways involving potassium channel regulation and thromboxane signaling to be implicated. This integrative metabolomic and genetic investigation supports a potential protective role of glycoursodeoxycholic acid in cardiovascular vulnerability and motivates larger, carfilzomib-specific studies to evaluate its utility for risk stratification.

Abstract Mitral valve prolapse (MVP) is the most common cause of primary mitral regurgitation and is associated with the development of malignant arrhythmias, often in the context of myocardial fibrosis. The genetic architecture of MVP, and whether there are genetic factors explaining why only some individuals with MVP have adverse outcomes, remains poorly understood. We performed a meta-analysis of genome-wide association studies (GWAS) for MVP encompassing 21,517 cases among a total sample size of over 2.2 million individuals. We discovered 89 genomic risk loci for MVP, of which 72 were novel findings. Prioritization of causal genes and pathways using epigenetic and transcriptomic data from mitral valve and extra-valvular tissues replicated known gene associations to MVP including those involved in TGF-β signaling and extracellular matrix biology, but additionally emphasized a role in MVP for biological pathways relevant to cardiomyocyte biology. Accordingly, we identified several MVP risk loci with pleiotropy to cardiomyopathies, especially hypertrophic cardiomyopathy, and demonstrated a significant genetic correlation between MVP and hypertrophic cardiomyopathy. Finally, we interrogated snRNA-seq data in human papillary muscle tissue from two individuals with severe MVP, characterizing genes associated with both risk of papillary muscle fibrosis and MVP.

BACKGROUND:Joint use of multiple molecular layers can be useful to prioritize targets for mechanistic studies. Application of this approach to coronary disease in large populations is an emerging field. METHODS:We used reported circulating proteomic data (Somascan aptamer-based) from ≈3000 individuals in the CARDIA study (Coronary Artery Risk Development in Young Adults), measuring association with prevalent and 10-year incident coronary artery calcium (CAC) score. We used a multiparametric approach to prioritize circulating protein-CAC associations via genomics of circulating protein levels and coronary artery transcription. RESULTS:Proteins linked to prevalent/incident CAC in CARDIA implicated pathogenic mechanisms of vascular disease, including fibrosis and inflammation (GDF-15 [growth/differentiation factor 15], CDCP1 [CUB domain-containing protein 1], GSN [gelsolin], THBS2 [thrombospondin-2], chemokines, RNAS6 [ribonuclease K6]), oxidative lipid metabolism (CILP2; cartilage intermediate layer protein 2), extracellular matrix remodeling and signaling (MMPs [matrix metalloproteinases], TIMP-1 [tissue inhibitor of metalloproteinases 1], integrins), calcification (Notch 1, ARHGAP36 [Rho GTPase-activating protein 36]), and metabolism (GIP [gastric inhibitory polypeptide]), as well as new proteins not previously reported. Using proteome-wide association study genetic approaches, several targets with nominal evidence in CAC proteomics were associated with atherosclerosis or myocardial infarction in over 300K individuals, including PCSK9 (proprotein convertase subtilisin/kexin type 9) and APOC1. Finally, the coronary artery-specific transcriptome-wide association study of CAC yielded genes with previously implicated mechanistic roles in vascular homeostasis, inflammation, and metabolism, as well as genes without previously described function in CAC. Overlap across CAC proteomics and transcriptome-wide association study highlighted genes involved in vascular inflammation (S100A9), cardiac development (HES1 [transcription factor HES-1]), vessel wall structure (SPARCL1 [SPARC-like protein 1]), and vascular dysfunction or plaque (NOTCH3 [neurogenic locus notch homolog protein 3], TNFSF12 [tumor necrosis factor ligand superfamily member 12], S100A12 [protein S100-A12]). CONCLUSIONS:These results report population-level multiomics in human coronary calcification, presenting a method to identify disease-relevant targets through integration of human genetic approaches with multiomics.

Heart transplantation (HT) is the durable therapy for end-stage heart failure (HF). Despite advances in immunosuppression, cardiac allograft vasculopathy (CAV) remains a leading cause of late graft failure and mortality in the modern era. Prior studies have established donor age and immunological phenomena, such as acute cellular rejection (ACR), antibody-mediated rejection (AMR), and development of donor-specific antibodies (DSAs) as risk factors for CAV1-5. However, it remains unclear whether acute rejection (AR) that occurs early post-HT, when individuals experience the highest degree of immunosuppression, reflects higher baseline immune activity and confers a higher risk of future CAV compared to later AR, when immunosuppression is minimized. We therefore examined whether AR occurring during pre-specified early and intermediate intervals compared to those who did not experience AR in the first post-HT year was associated with future CAV among recipients without CAV at 1 year.

Allograft rejection following solid-organ transplantation is a major cause of graft dysfunction and mortality. Current diagnostic approaches rely on histology, which exhibits wide diagnostic variability and lacks clinically relevant molecular phenotyping. Here we leverage image-based spatial transcriptomics at subcellular resolution in longitudinal human cardiac biopsies to characterize transcriptional heterogeneity in 62 adult and pediatric heart transplant recipients during and following histologically diagnosed rejection. Across 28 cell types, we identified significant differences in abundance in immune and parenchymal cells across different classes of rejection. We observed broad overlap in transcriptional states across rejection severity and significant heterogeneity within rejection grades. Responders and nonresponders to augmented therapies had distinct transcriptomic profiles, with nonresponders exhibiting baseline T cell hyperactivation and tissue remodeling genes. We also identified cell-specific genes linked to long-term outcomes after heart transplant. These results underscore the importance of subtyping cellular states during rejection to stratify immune-cardiac interactions relevant to short- and long-term outcomes.

Introduction: The prevalence of hepatic steatosis—a central and early phenotype in multi-system metabolic dysfunction—is increasing in parallel with the obesity pandemic, calling for novel approaches for prevention and treatment. Hypothesis: We hypothesized that the circulating proteome may reflect cell specific mechanisms of hepatic steatosis. Methods: Using multi-modality hepatic imaging and broad circulating proteomics in approximately 5,000 individuals across 3 diverse cohorts (CARDIA, Cameron County Hispanic Cohort, UK Biobank), we identified proteins implicated in the progression of hepatic steatosis. We tested for a relationship with these proteomic markers of hepatic steatosis with metabolic-related clinical outcomes in UK Biobank. We translated these findings from the circulating proteome to several tissue-based datasets including bulk RNA sequencing, single-cell RNA sequencing, and spatial transcriptomics. To further prove the hepatocyte origin of prioritized proteins, we used a humanized “liver-on-a-chip” model. Results: We observed proteins implicated in the progression of hepatic steatosis—such as those related to central carbon and amino acid metabolism, hepatocyte regeneration, inflammation, fibrosis, insulin sensitivity—are largely encoded by genes enriched at the transcriptional level in human liver. Circulating multi-protein signatures of hepatic steatosis were strongly associated with a fatty liver disease phenotype and multi-system metabolic outcomes in >26,000 free-living individuals. Moreover, we observed increased activity of transcripts encoding proteins prioritized in clinical studies spatially in areas of steatosis via spatial transcriptomics in human liver, with several top candidates dynamic during progression of steatosis in human liver. Finally, using a humanized “liver-on-a-chip” model, we induced hepatic steatosis, confirming cell-specific expression of targets implicated across tissue and clinical studies at a transcriptional and proteomic level. Conclusions: These results underscore the utility of a unified approach that combines human studies, multi-omics, and dynamic tissue-on-a-chip experiments to identify a prognostic, functional, dynamic “liquid biopsy” of human liver, with relevance for clinical biomarker discovery and mechanistic research applications.

Background Heart transplantation (HT) is the definitive therapy for end-stage heart failure. However, with improving post-HT survival in the modern era, recipients are increasingly cumulatively exposed to unique risk factors for cardiovascular-kidney-metabolic (CKM) dysfunction. An expanded understanding of the incidence and prevalence of CKM dysfunction post-HT may inform screening and therapeutic strategies to mitigate adverse events. Objectives The aim of this study was to characterize the incidence and prevalence of CKM risk factors in adult and pediatric HT recipients and to define their association with cardiac allograft vasculopathy (CAV) and mortality. Methods A single-center retrospective observational study was conducted in adults and children who underwent HT between January 1, 2015, and June 30, 2024. Longitudinal clinical and laboratory data were extracted from the electronic health record. Incidence rates (IRs) of type 2 diabetes mellitus (DM2), overweight or obesity, hypertension, chronic kidney disease (CKD), and dyslipidemia were calculated. longitudinal trajectories of CKM dysfunction were constructed, and the impact of sodium-glucose cotransporter 2 (SGLT2) inhibitors and glucagon-like peptide-1 receptor agonists (GLP1RAs) on CKD and body mass index, respectively, was evaluated. Finally, immunologic and CKM comorbidities were linked with clinical outcomes by using time-varying Cox regression models. Results During the study period, 860 adults and 84 children underwent HT. Among adults, the IRs (reported as cases per 100 person-years) of DM2, overweight or obesity, dyslipidemia, and CKD were 28.6, 77.3, 139.1, and 69.7, respectively. Among children, the IRs of DM2, overweight or obesity, dyslipidemia, and CKD were 2.8, 26.9, 5.5, and 3.6. Within 12 months post-HT, 99% of adults developed stage 1 or 2 hypertension, and 22.1% of all adults developed hemoglobin A1c levels ≥6.5%, regardless of a preexisting diagnosis of DM2. Similarly, 37.5% of adults developed moderate to severe hypertriglyceridemia and 31.1% manifested worsened low-density lipoprotein cholesterol control. Among adults with an estimated glomerular filtration rate (eGFR) ≥45 mL/min/1.73 m2 pre-HT, 86.2% displayed worsening renal function (eGFR <45 mL/min/1.73 m2) within 12 months post-HT. In adults initiated on SGLT2 inhibitor post-HT (n = 242), there was a nonlinear improvement in eGFR during the ensuing 12 months; for individuals initiated on GLP1RAs (n = 168), there was a predominantly linear reduction in body mass index. Among CKM comorbidities, none was significantly associated with CAV, whereas DM2 was associated with increased post-HT mortality (HR: 1.84; 95% CI: 1.04-3.25). Conclusions A significant proportion of HT recipients experience new-onset or worsening CKM dysfunction after HT. Furthermore, CKM comorbidities are associated with post-HT mortality. These results indicate that SGLT2 inhibitors and GLP1RAs may mitigate the burden of CKM disease.

Introduction: Percutaneous ventricular assist devices (pVADs) are increasingly used in cardiogenic shock but are associated with complications including haemolysis. The aim of this study was to investigate patient characteristics associated with haemolysis in cardiogenic shock patient population. Methods: Consecutive patients were identified using Current Procedural Terminology (CPT) codes for pVAD insertion. Patient characteristics, laboratory and imaging data, and patient outcomes were abstracted manually and using validated automated methods. Laboratory-defined haemolysis required a drop in haemoglobin >= 2 mg/dl with either lactate dehydrogenase >= 250 units/l or undetectable haptoglobin. Clinically significant haemolysis was defined as laboratory-defined haemolysis necessitating transfusion. Primary outcome was the association between haemolysis and on-device and 30-day mortality. Results: A total of 196 patients underwent pVAD insertion for cardiogenic shock during the study period and were included. Laboratory-defined haemolysis occurred in 46 patients (23.5%), of whom 12 (6.1%) had clinically significant haemolysis. Haemolysis occurred more often following emergency insertion, rather than elective insertion (84.8% versus 40.0%, p<0.001) in patients with elevated lactic acid levels (median 2.5 versus 1.6, p=0.016) and elevated heart rates (92.5 BPM versus 86.5 BPM, p=0.023). After multivariable adjustment, there was no association between laboratory-defined haemolysis and on-device (OR 0.6; 95% CI [0.1-3.4]; p=0.565) or 30-day mortality (OR 2.1; 95% CI [0.4-13.0]; p=0.391). Conclusion: Laboratory-defined haemolysis was common in patients with cardiogenic shock and pVAD, but clinically significant haemolysis was not. There was no association between haemolysis and on-device or 30-day mortality.

Context Repetitive bouts of weight loss and regain, termed weight cycling, may exaggerate the risk for cardiometabolic disease. We previously identified that weight cyclers were likely to have prescribed medications for hypertension, dyslipidemia, and diabetes-suggesting a high prevalence of cardiometabolic disease.Objective No prior study has compared relationships between longitudinal weight trajectory (weight stable, weight gainer, weight loser, or weight cycler) and commonly occurring specific cardiometabolic diseases among persons with similar high baseline body mass index (BMI).Methods Using de-identified electronic health record data from all adults treated at Vanderbilt University Medical Center between 1997 to 2020 and a landmark approach, we developed multivariate Cox proportional hazards regression models to determine relationships between weight trajectory and risk for 10 highly prevalent cardiometabolic diseases.Results Compared to weight stability, weight cycling associated with an almost 30% increased risk for obstructive sleep apnea [hazard ratio (HR) 1.28; 95% confidence interval (CI) 1.15-1.42], metabolic dysfunction-associated steatotic liver disease (HR 1.28; 95% CI 1.08-1.51), and type 2 diabetes (HR 1.23; 95% CI 1.10-1.38). Weight cycling also associated with a more than 50% increased risk for heart failure (HR 1.54; 95% CI 1.31-1.82), although both weight gain and weight loss also showed increased risk for heart failure (HR 1.29; 95% CI 1.08-1.55 and HR 1.32; 95% CI 1.10-1.58, respectively).Conclusion The relationship between weight cycling and cardiometabolic disease risk was independent of having high baseline BMI, which was similar among weight trajectory groups. The present findings support promoting either weight stability at high BMI or weight loss if able to be maintained to prevent the incidence of a variety of cardiometabolic diseases.

Background The effect of recent onset metabolic dysfunction on coronary artery disease (CAD) risk is poorly understood. We developed a large data set linking metabolic phenotypes and clinical outcomes to quantify CAD risks associated with adverse metabolic transition. Methods Clinical parameters, measures of metabolic dysfunction components (diabetes, hypertension, elevated triglycerides, and low high‐density lipoprotein) and incident CAD were curated in a clinical cohort from a single quaternary medical center. Associations between body weight, metabolic abnormalities, and changes in metabolic health status were assessed for prevalent and incident CAD. Results Increasing body mass index category, presence of metabolic dysfunction (>2 components), and number of metabolic abnormalities were associated with prevalent CAD among 844 841 individuals. In time‐to‐event analyses (N=69 272), ~38% of subjects initially free of any metabolic dysfunction abnormality developed ≥1 abnormality over a 3‐year run‐in period. All metabolic abnormalities, whether new‐onset or preexisting, significantly increased 10‐year CAD event probability, and there was a progressive increase in 10‐year CAD risk with increasing burden of metabolic abnormalities. In models adjusting for metabolic dysfunction, 10‐year CAD risk increased for individuals with body mass index >25 kg/m2 (hazard ratio, 3.8). Compared with individuals with a body mass index of 25 kg/m2, any short‐term weight gain or loss of >5% increased or decreased CAD risk, respectively, in individuals with a body mass index of 35 kg/m2. Conclusions In a large clinical cohort, the transition to metabolic dysfunction is common, occurs rapidly, and significantly increases incident CAD risk. The effect of body weight and weight loss on CAD risk is nonlinear. Interventions to prevent progression to metabolic dysfunction are needed.

Metabolic dysfunction-associated steatotic liver disease (MASLD) - characterized by excess accumulation of fat in the liver - now affects one-third of the world's population. As MASLD progresses, extracellular matrix components including collagen accumulate in the liver, causing tissue fibrosis, a major determinant of disease severity and mortality. To identify transcriptional regulators of fibrosis, we computationally inferred the activity of transcription factors (TFs) relevant to fibrosis by profiling the matched transcriptomes and epigenomes of 108 human liver biopsies from a deeply characterized cohort of patients spanning the full histopathologic spectrum of MASLD. CRISPR-based genetic KO of the top 100 TFs identified ZNF469 as a regulator of collagen expression in primary human hepatic stellate cells (HSCs). Gain- and loss-of-function studies established that ZNF469 regulates collagen genes and genes involved in matrix homeostasis through direct binding to gene bodies and regulatory elements. By integrating multiomic large-scale profiling of human biopsies with extensive experimental validation, we demonstrate that ZNF469 is a transcriptional regulator of collagen in HSCs. Overall, these data nominate ZNF469 as a previously unrecognized determinant of MASLD-associated liver fibrosis.

Pressure overload initiates a series of alterations in the human heart that predate macroscopic organ-level remodeling and downstream heart failure. We study aortic stenosis through integrated proteomic, tissue transcriptomic, and genetic methods to prioritize targets causal in human heart failure. First, we identify the circulating proteome of cardiac remodeling in aortic stenosis, specifying known and previously-unknown mediators of fibrosis, hypertrophy, and oxidative stress, several associated with interstitial fibrosis in a separate cohort (N = 145). These signatures are strongly related to clinical outcomes in aortic stenosis (N = 802) and in broader at-risk populations in the UK Biobank (N = 36,668). We next map this remodeling proteome to myocardial transcription in patients with and without aortic stenosis through single-nuclear transcriptomics, observing broad differential expression of genes encoding this remodeling proteome, featuring fibrosis pathways and metabolic-inflammatory signaling. Finally, integrating our circulating and tissue-specific results with modern genetic approaches, we implicate several targets as causal in heart failure.

Rationale: Accelerated decline in lung function is associated with incident chronic obstructive pulmonary disease (COPD), hospitalization, and death. However, identifying this trajectory with longitudinal spirometry measurements is challenging in clinical practice. Objectives: To determine whether a proteomic risk score trained on accelerated decline in lung function can assess the risk of future respiratory disease and mortality. Methods: In the Coronary Artery Risk Development in Young Adults Study, a population-based cohort starting in young adulthood, longitudinal measurements of FEV1 percent predicted (up to six time points over 30 yr) were used to identify accelerated and normal decline trajectories. Protein aptamers associated with an accelerated decline trajectory were identified with multivariable logistic regression followed by LASSO (least absolute shrinkage and selection operator) regression. The proteomic respiratory susceptibility score was derived on the basis of these circulating proteins and applied to the U.K. Biobank (UKBB) and COPDGene studies to examine associations with future respiratory morbidity and mortality. Measurements and Main Results: Higher susceptibility score was independently associated with all-cause mortality (UKBB hazard ratio [HR], 1.56; 95% confidence interval [CI], 1.50-1.61; COPDGene HR, 1.75 95% CI, 1.63-1.88), respiratory mortality (UKBB HR, 2.39; 95% CI, 2.16-2.64; COPDGene HR, 1.81; 95% CI, 1.32-2.47), incident COPD (UKBB HR, 1.84; 95% CI, 1.71-1.98), incident respiratory exacerbation (COPDGene odds ratio, 1.10; 95% CI, 1.02-1.19), and incident exacerbation requiring hospitalization (COPDGene OR, 1.17; 95% CI, 1.08-1.27). Conclusions: A proteomic signature of increased respiratory susceptibility identifies people at risk of respiratory death, incident COPD, and respiratory exacerbations. This susceptibility score is composed of proteins with well-known and novel associations with lung health and holds promise for the early detection of lung disease without requiring years of spirometry measurements.