Redefining the molecular rejection states in 3230 heart transplant biopsies: Relationships to parenchymal injury and graft survival.

The first-generation Molecular Microscope (MMDx) system for heart transplant endomyocardial biopsies used expression of rejection-associated transcripts (RATs) to diagnose not only T cell-mediated rejection (TCMR) and antibody-mediated rejection (ABMR) but also acute injury. However, the ideal system should detect rejection without being influenced by injury, to permit analysis of the relationship between rejection and parenchymal injury. To achieve this, we developed a new rejection classification in an expanded cohort of 3230 biopsies: 1641 from INTERHEART (ClinicalTrials.gov NCT02670408), plus 1589 service biopsies added to improve the power of the machine learning algorithms. The new system used 6 rejection classifiers instead of RATs and generated 7 rejection archetypes: No rejection, 48%; Minor, 24%; TCMR1, 2.3%; TCMR2, 2.7%; TCMR/mixed, 2.7%; early-stage ABMR, 3.9%; and fully developed ABMR, 16%. Using rejection classifiers eliminated cross-reactions with acute injury, permitting separate assessment of rejection and injury. TCMR was associated with severe-recent injury and late atrophy-fibrosis and rarely had normal parenchyma. ABMR was better tolerated, seldom producing severe injury, but in later biopsies was often associated with atrophy-fibrosis, indicating long-term risk. Graft survival and left ventricular ejection fraction were reduced not only in hearts with TCMR but also in hearts with severe-recent injury and atrophy-fibrosis, even without rejection.

Unsupervised mRNA-seq classification of heart transplant endomyocardial biopsies.

BACKGROUND: Endomyocardial biopsy (EMB) is currently considered the gold standard for diagnosing cardiac allograft rejection. However, significant limitations related to histological interpretation variability are well-recognized. We sought to develop a methodology to evaluate EMB solely based on gene expression, without relying on histology interpretation. METHODS: Sixty-four EMBs were obtained from 47 post-heart transplant recipients, who were evaluated for allograft rejection. EMBs were subjected to mRNA sequencing, in which an unsupervised classification algorithm was used to identify the molecular signatures that best classified the EMBs. Cytokine and natriuretic peptide peripheral blood profiling was also performed. Subsequently, we performed gene network analysis to identify the gene modules and gene ontology to understand their biological relevance. We correlated our findings with the unsupervised and histological classifications. RESULTS: Our algorithm classifies EMBs into three categories based solely on clusters of gene expression: unsupervised classes 1, 2, and 3. Unsupervised and histological classifications were closely related, with stronger gene module-phenotype correlations for the unsupervised classes. Gene ontology enrichment analysis revealed processes impacting on the regulation of cardiac and mitochondrial function, immune response, and tissue injury response. Significant levels of cytokines and natriuretic peptides were detected following the unsupervised classification. CONCLUSION: We have developed an unsupervised algorithm that classifies EMBs into three distinct categories, without relying on histology interpretation. These categories were highly correlated with mitochondrial, immune, and tissue injury response. Significant cytokine and natriuretic peptide levels were detected within the unsupervised classification. If further validated, the unsupervised classification could offer a more objective EMB evaluation.

Adaptive Content Tuning of Social Network Digital Health Interventions Using Control Systems Engineering for Precision Public Health: Cluster Randomized Controlled Trial.

BACKGROUND: Social media has emerged as an effective tool to mitigate preventable and costly health issues with social network interventions (SNIs), but a precision public health approach is still lacking to improve health equity and account for population disparities. OBJECTIVE: This study aimed to (1) develop an SNI framework for precision public health using control systems engineering to improve the delivery of digital educational interventions for health behavior change and (2) validate the SNI framework to increase organ donation awareness in California, taking into account underlying population disparities. METHODS: This study developed and tested an SNI framework that uses publicly available data at the ZIP Code Tabulation Area (ZCTA) level to uncover demographic environments using clustering analysis, which is then used to guide digital health interventions using the Meta business platform. The SNI delivered 5 tailored organ donation-related educational contents through Facebook to 4 distinct demographic environments uncovered in California with and without an Adaptive Content Tuning (ACT) mechanism, a novel application of the Proportional Integral Derivative (PID) method, in a cluster randomized trial (CRT) over a 3-month period. The daily number of impressions (ie, exposure to educational content) and clicks (ie, engagement) were measured as a surrogate marker of awareness. A stratified analysis per demographic environment was conducted. RESULTS: Four main clusters with distinctive sociodemographic characteristics were identified for the state of California. The ACT mechanism significantly increased the overall click rate per 1000 impressions (ß=.2187; P<.001), with the highest effect on cluster 1 (ß=.3683; P<.001) and the lowest effect on cluster 4 (ß=.0936; P=.053). Cluster 1 is mainly composed of a population that is more likely to be rural, White, and have a higher rate of Medicare beneficiaries, while cluster 4 is more likely to be urban, Hispanic, and African American, with a high employment rate without high income and a higher proportion of Medicaid beneficiaries. CONCLUSIONS: The proposed SNI framework, with its ACT mechanism, learns and delivers, in real time, for each distinct subpopulation, the most tailored educational content and establishes a new standard for precision public health to design novel health interventions with the use of social media, automation, and machine learning in a form that is efficient and equitable. TRIAL REGISTRATION: ClinicalTrials.gov NTC04850287; https://clinicaltrials.gov/ct2/show/NCT04850287.

Discovery of non-HLA antibodies associated with cardiac allograft rejection and development and validation of a non-HLA antigen multiplex panel: From bench to bedside.

We analyzed humoral immune responses to nonhuman leukocyte antigen (HLA) after cardiac transplantation to identify antibodies associated with allograft rejection. Protein microarray identified 366 non-HLA antibodies (>1.5 fold, P < .5) from a discovery cohort of HLA antibody-negative, endothelial cell crossmatch-positive sera obtained from 12 cardiac allograft recipients at the time of biopsy-proven rejection. From these, 19 plasma membrane proteins and 10 autoantigens identified from gene ontology analysis were combined with 48 proteins identified through literature search to generate a multiplex bead array. Longitudinal sera from a multicenter cohort of adult cardiac allograft recipients (samples: n = 477 no rejection; n = 69 rejection) identified 18 non-HLA antibodies associated with rejection (P < .1) including 4 newly identified non-HLA antigenic targets (DEXI, EMCN, LPHN1, and SSB). CART analysis showed 5/18 non-HLA antibodies distinguished rejection vs nonrejection. Antibodies to 4/18 non-HLA antigens synergize with HLA donor-specific antibodies and significantly increase the odds of rejection (P < .1). The non-HLA panel was validated using an independent adult cardiac transplant cohort (n = 21 no rejection; n = 42 rejection, >1R) with an area under the curve of 0.87 (P < .05) with 92.86% sensitivity and 66.67% specificity. We conclude that multiplex bead array assessment of non-HLA antibodies identifies cardiac transplant recipients at risk of rejection.

A Data-Driven Social Network Intervention for Improving Organ Donation Awareness Among Minorities: Analysis and Optimization of a Cross-Sectional Study.

BACKGROUND: Increasing the number of organ donors may enhance organ transplantation, and past health interventions have shown the potential to generate both large-scale and sustainable changes, particularly among minorities. OBJECTIVE: This study aimed to propose a conceptual data-driven framework that tracks digital markers of public organ donation awareness using Twitter and delivers an optimized social network intervention (SNI) to targeted audiences using Facebook. METHODS: We monitored digital markers of organ donation awareness across the United States over a 1-year period using Twitter and examined their association with organ donation registration. We delivered this SNI on Facebook with and without optimized awareness content (ie, educational content with a weblink to an online donor registration website) to low-income Hispanics in Los Angeles over a 1-month period and measured the daily number of impressions (ie, exposure to information) and clicks (ie, engagement) among the target audience. RESULTS: Digital markers of organ donation awareness on Twitter are associated with donation registration (beta=.0032; P<.001) such that 10 additional organ-related tweets are associated with a 3.20% (33,933/1,060,403) increase in the number of organ donor registrations at the city level. In addition, our SNI on Facebook effectively reached 1 million users, and the use of optimization significantly increased the rate of clicks per impression (beta=.0213; P<.004). CONCLUSIONS: Our framework can provide a real-time characterization of organ donation awareness while effectively delivering tailored interventions to minority communities. It can complement past approaches to create large-scale, sustainable interventions that are capable of raising awareness and effectively mitigate disparities in organ donation.

Psychiatric Illness in Takotsubo (Stress) Cardiomyopathy: A Review.

BACKGROUND: Takotsubo cardiomyopathy (TC), also known as stress-induced cardiomyopathy, has been increasingly described in relation to psychiatric illness. METHODS: We performed a literature review to identify the key findings related to psychiatric illness in TC that may be relevant to the practice of mental health and other health care providers. RESULTS: The association of psychiatric illness with TC in addition to the spectrum of psychiatric illness found in TC, the role of exacerbation or treatment of psychiatric illness in triggering TC, different modes of presentation, prognostic implications, and long-term management of psychiatric illness in TC are discussed. Additionally, we review the limitations of the pre-existing literature and suggest areas of future research. CONCLUSIONS: There is a strong association between pre-existing psychiatric illness, particularly anxiety and mood spectrum disorders, and TC. Acute exacerbation of psychiatric illness, rapid uptitration or overdose of certain psychotropic agents, and electroconvulsive therapy may trigger TC. Further studies are needed to better evaluate the prognostic significance and long-term management of psychiatric illness in TC.

Pre-existing Psychiatric Illness is Associated With Increased Risk of Recurrent Takotsubo Cardiomyopathy.

BACKGROUND: The increased prevalence of psychiatric illness among patients with takotsubo cardiomyopathy (TC) has been previously described. OBJECTIVES: We sought to assess the effect of pre-existing psychiatric illness on clinical outcomes following the diagnosis of TC. METHODS: Adults diagnosed with TC at Vanderbilt University Medical Center between 1999 and 2015 were included in the study. Medical records were retrospectively reviewed to identify any pre-existing mood, anxiety, or schizophrenia-spectrum illness before TC presentation. Multivariable logistic regression was used to test for independent association of pre-existing psychiatric illness with 30-day mortality and recurrent TC; Cox proportional hazard analysis was used to evaluate for association with long-term mortality. RESULTS: Among 306 patients diagnosed with TC during the study period, 114 (37%) had a pre-existing psychiatric illness. In all, 43 (14%) and 88 (29%) patients died within 30 days of index diagnosis and as of last medical record review, respectively. Of the 269 who survived their index hospitalization, 19 (7%) had a confirmed recurrent episode of TC. In multivariable analyses, pre-existing psychiatric illness was not associated with increased 30-day (P = 0.320) or long-term (P = 0.621) mortality. Pre-existing psychiatric illness was associated with higher risk of recurrent TC (odds ratio = 7.44, 95% CI: 2.30-24.01, P < 0.001). CONCLUSIONS: Pre-existing psychiatric illness was associated with an increased risk of recurrent TC. No significant association was noted between pre-existing psychiatric illness and survival.

Regulation of acetylation restores proteolytic function of diseased myocardium in mouse and human.

Proteasome complexes play essential roles in maintaining cellular protein homeostasis and serve fundamental roles in cardiac function under normal and pathological conditions. A functional detriment in proteasomal activities has been recognized as a major contributor to the progression of cardiovascular diseases. Therefore, approaches to restore proteolytic function within the setting of the diseased myocardium would be of great clinical significance. In this study, we discovered that the cardiac proteasomal activity could be regulated by acetylation. Histone deacetylase (HDAC) inhibitors (suberoylanilide hydroxamic acid and sodium valproate) enhanced the acetylation of 20S proteasome subunits in the myocardium and led to an elevation of proteolytic capacity. This regulatory paradigm was present in both healthy and acutely ischemia/reperfusion (I/R) injured murine hearts, and HDAC inhibition in vitro restored proteolytic capacities to baseline sham levels in injured hearts. This mechanism of regulation was also viable in failing human myocardium. With 20S proteasomal complexes purified from murine myocardium treated with HDAC inhibitors in vivo, we confirmed that acetylation of 20S subunits directly, at least in part, presents a molecular explanation for the improvement in function. Furthermore, using high-resolution LC-MS/MS, we unraveled the first cardiac 20S acetylome, which identified the acetylation of nine N-termini and seven internal lysine residues. Acetylation on four lysine residues and four N-termini on cardiac proteasomes were novel discoveries of this study. In addition, the acetylation of five lysine residues was inducible via HDAC inhibition, which correlated with the enhancement of 20S proteasomal activity. Taken as a whole, our investigation unveiled a novel mechanism of proteasomal function regulation in vivo and established a new strategy for the potential rescue of compromised proteolytic function in the failing heart using HDAC inhibitors.

Molecular basis of recovering on mechanical circulatory support.

Our insights into different system levels of mechanisms by left ventricular assist device support are increasing and suggest a complex regulatory system of overlapping biological processes. To develop novel decision-making strategies and patient selection criteria, heart failure and reverse cardiac remodeling should be conceptualized and explored by a multifaceted research strategy of transcriptomics, metabolomics, proteomics, molecular biology, and bioinformatics. Knowledge of the molecular mechanisms of reverse cardiac remodeling is in its early stages, and comprehensive reconstruction of the underlying networks is necessary.

Clinical, Echocardiographic, and Longitudinal Characteristics Associated With Heart Failure With Improved Ejection Fraction.

Heart failure with improved ejection fraction (HFimpEF) has better outcomes than HF with reduced EF (HFrEF). However, factors contributing to HFimpEF remain unclear. This study aimed to evaluate clinical and longitudinal characteristics associated with subsequent HFimpEF. This was a single-center retrospective HFrEF cohort study. Data were collected from 2014 to 2022. Patients with HFrEF were identified using International Classification of Diseases codes, echocardiographic data, and natriuretic peptide levels. The main end points were HFimpEF (defined as EF >40% at ≥3 months with ≥10% increase) and mortality. Cox proportional hazards and mixed effects models were used for analyses. The study included 1,307 patients with HFrEF with a median follow-up of 16.3 months (interquartile range 8.0 to 30.6). The median age was 65 years; 68% were male whereas 57% were White. On follow-up, 38.7% (n = 506) developed HFimpEF, whereas 61.3% (n = 801) had persistent HFrEF. A multivariate Cox regression model identified gender, race, co-morbidities, echocardiographic, and natriuretic peptide as significant covariates of HFimpEF (p <0.05). The HFimpEF group had better survival compared with the persistent HFrEF group (p <0.001). Echocardiographic and laboratory trajectories differed between groups. In this HFrEF cohort, 38.7% transitioned to HFimpEF and approximately 50% met the definition within the first 12 months. In a HFimpEF model, gender, co-morbidities, echocardiographic parameters, and natriuretic peptide were associated with subsequent HFimpEF. The model has the potential to identify patients at risk of subsequent persistent or improved HFrEF, thus informing the design and implementation of targeted quality-of-care improvement interventions.

Molecular states associated with dysfunction and graft loss in heart transplants.

BACKGROUND: We explored the changes in gene expression correlating with dysfunction and graft failure in endomyocardial biopsies. METHODS: Genome-wide microarrays (19,462 genes) were used to define mRNA changes correlating with dysfunction (left ventricular ejection fraction [LVEF] ≤ 55) and risk of graft loss within 3 years postbiopsy. LVEF data was available for 1,013 biopsies and survival data for 779 patients (74 losses). Molecular classifiers were built for predicting dysfunction (LVEF ≤ 55) and postbiopsy 3-year survival. RESULTS: Dysfunction is correlated with dedifferentiation-decreased expression of normal heart transcripts, for example, solute carriers, along with increased expression of inflammation genes. Many genes with reduced expression in dysfunction were matrix genes such as fibulin 1 and decorin. Gene ontology (GO) categories suggested matrix remodeling and inflammation, not rejection. Genes associated with the risk of failure postbiopsy overlapped dysfunction genes but also included genes affecting microcirculation, for example, arginase 2, which reduces NO production, and endothelin 1. GO terms also reflected increased glycolysis and response to hypoxia, but decreased VEGF and angiogenesis pathways. T cell-mediated rejection was associated with reduced survival and antibody-mediated rejection with relatively good survival, but the main determinants of survival were features of parenchymal injury. Both dysfunction and graft loss were correlated with increased biopsy expression of BNP (gene NPPB). Survival probability classifiers divided hearts into risk quintiles, with actuarial 3-year postbiopsy survival >95% for the highest versus 50% for the lowest. CONCLUSIONS: Dysfunction in transplanted hearts reflects dedifferentiation, decreased matrix genes, injury, and inflammation. The risk of short-term loss includes these changes but is also associated with microcirculation abnormalities, glycolysis, and response to hypoxia.

Technology platform development for targeted plasma metabolites in human heart failure.

BACKGROUND: Heart failure is a multifactorial disease associated with staggeringly high morbidity and motility. Recently, alterations of multiple metabolites have been implicated in heart failure; however, the lack of an effective technology platform to assess these metabolites has limited our understanding on how they contribute to this disease phenotype. We have successfully developed a new workflow combining specific sample preparation with tandem mass spectrometry that enables us to extract most of the targeted metabolites. 19 metabolites were chosen ascribing to their biological relevance to heart failure, including extracellular matrix remodeling, inflammation, insulin resistance, renal dysfunction, and cardioprotection against ischemic injury. RESULTS: In this report, we systematically engineered, optimized and refined a protocol applicable to human plasma samples; this study contributes to the methodology development with respect to deproteinization, incubation, reconstitution, and detection with mass spectrometry. The deproteinization step was optimized with 20% methanol/ethanol at a plasma:solvent ratio of 1:3. Subsequently, an incubation step was implemented which remarkably enhanced the metabolite signals and the number of metabolite peaks detected by mass spectrometry in both positive and negative modes. With respect to the step of reconstitution, 0.1% formic acid was designated as the reconstitution solvent vs. 6.5 mM ammonium bicarbonate, based on the comparable number of metabolite peaks detected in both solvents, and yet the signal detected in the former was higher. By adapting this finalized protocol, we were able to retrieve 13 out of 19 targeted metabolites from human plasma. CONCLUSIONS: We have successfully devised a simple albeit effective workflow for the targeted plasma metabolites relevant to human heart failure. This will be employed in tandem with high throughput liquid chromatography mass spectrometry platform to validate and characterize these potential metabolic biomarkers for diagnostic and therapeutic development of heart failure patients.

Insights and perspectives into clinical biomarker discovery in pediatric heart failure and congenital heart disease-a narrative review.

Background and Objective: Heart failure (HF) in the pediatric population is a multi-factorial process with a wide spectrum of etiologies and clinical manifestations, that are distinct from the adult HF population, with congenital heart disease (CHD) as the most common cause. CHD has high morbidity/mortality with nearly 60% developing HF during the first 12 months of life. Hence, early discovery and diagnosis of CHD in neonates is pivotal. Plasma B-type natriuretic peptide (BNP) is an increasingly popular clinical marker in pediatric HF, however, in contrast to adult HF, it is not yet included in pediatric HF guidelines and there is no standardized reference cut-off value. We explore the current trends and prospects of biomarkers in pediatric HF, including CHD that can aid in diagnosis and management. Methods: As a narrative review, we will analyze biomarkers with respect to diagnosis and monitoring in specific anatomical types of CHD in the pediatric population considering all English PubMed publications till June 2022. Key Content and Findings: We present a concise description of our own experience in applying plasma BNP as a clinical biomarker in pediatric HF and CHD (tetralogy of fallot vs. ventricular septal defect) in the context of surgical correction, as well as untargeted metabolomics analyses. In the current age of Information Technology and large data sets we also explored new biomarker discovery using Text Mining of 33M manuscripts currently on PubMed. Conclusions: (Multi) Omics studies from patient samples as well as Data Mining can be considered for the discovery of potential pediatric HF biomarkers useful in clinical care. Future research should focus on validation and defining evidence-based value limits and reference ranges for specific indications using the most up-to-date assays in parallel to commonly used studies.

Remote monitoring titration clinic to implement guideline-directed therapy for heart failure patients with reduced ejection fraction: a pilot quality-improvement intervention.

Introduction: Guideline-directed medical therapy (GDMT) is the recommended treatment for heart failure with reduced ejection fraction (HFrEF). However, the implementation remains limited, with suboptimal use and dosing. The study aimed to assess the feasibility and effect of a remote monitoring titration program on GDMT implementation. Methods: HFrEF patients were randomly assigned to receive either usual care or a quality-improvement remote titration with remote monitoring intervention. The intervention group used wireless devices to transmit heart rate, blood pressure, and weight data daily, which were reviewed by physicians and nurses every 2-4 weeks. Medication tolerance was assessed via phone, and dosage instructions were given. This workflow was repeated until target doses were reached or further adjustments were not tolerated. A 4-GDMT score measured use and target dosage, with the primary endpoint being the score at 6 months follow-up. Results: Baseline characteristics were similar (n = 55). A median of 85% of patients complied with transmitting device data every week. At the 6-month follow-up, the intervention group had a 4-GDMT score of 64.6% compared to 56.5% in the usual care group (p = 0.01), with a difference of 8.1% (95% CI: 1.7%-14.5%). Similar results were seen at the 12-month follow-up [difference 12.8% (CI: 5.0%-20.6%)]. The intervention group showed a positive trend in ejection fraction and natriuretic peptides, with no significant difference between groups. Conclusions: The study suggests that a full-scale trial is feasible and that utilizing a remote titration clinic with remote monitoring has the potential to enhance the implementation of guideline-directed therapy for HFrEF.

Clinical, Echocardiographic, and Longitudinal Characteristics Associated with Heart Failure with Improved Ejection Fraction.

Background: Heart failure (HF) with improved ejection fraction (HFimpEF) has better outcomes than HF with reduced ejection fraction (HFrEF). However, factors contributing to HFimpEF remain unclear. This study aimed to evaluate clinical and longitudinal characteristics associated with subsequent HFimpEF. Methods: This was a single-center retrospective HFrEF cohort study. Data were collected from 2014 to 2022. Patients with HFrEF were identified using ICD codes, echocardiographic data, and natriuretic peptide levels. The main endpoints were HFimpEF (defined as ejection fraction >40% at ≥3 months with ≥10% increase) and mortality. Cox proportional hazards and mixed effects models were used for analyses. Results: The study included 1307 HFrEF patients with a median follow-up of 16.3 months (IQR 8.0-30.6). The median age was 65 years; 68% were male while 57% were white. On follow-up, 39% (n=506) developed HFimpEF, while 61% (n=801) had persistent HFrEF. A multivariate Cox regression model identified sex, race comorbidities, echocardiographic, and natriuretic peptide as significant covariates of HFimpEF ( p <0.05). The HFimpEF group had better survival compared to the persistent HFrEF group ( p <0.001). Echocardiographic and laboratory trajectories differed between groups. Conclusion: In this HFrEF cohort, 39% transitioned to HFimpEF and approximately 50% met the definition within the first 12 months. In a HFimpEF model, sex, comorbidities, echocardiographic parameters, and natriuretic peptide were associated with subsequent HFimpEF. The model has the potential to identify patients at risk of subsequent persistent or improved HFrEF, thus informing the design and implementation of targeted quality-of-care improvement interventions.

The Association of Shared Care Networks With 30-Day Heart Failure Excessive Hospital Readmissions: Longitudinal Observational Study.

BACKGROUND: Higher-than-expected heart failure (HF) readmissions affect half of US hospitals every year. The Hospital Reduction Readmission Program has reduced risk-adjusted readmissions, but it has also produced unintended consequences. Shared care models have been advocated for HF care, but the association of shared care networks with HF readmissions has never been investigated. OBJECTIVE: This study aims to evaluate the association of shared care networks with 30-day HF excessive readmission rates using a longitudinal observational study. METHODS: We curated publicly available data on hospital discharges and HF excessive readmission ratios from hospitals in California between 2012 and 2017. Shared care areas were delineated as data-driven units of care coordination emerging from discharge networks. The localization index, the proportion of patients who reside in the same shared care area in which they are admitted, was calculated by year. Generalized estimating equations were used to evaluate the association between the localization index and the excessive readmission ratio of hospitals controlling for race/ethnicity and socioeconomic factors. RESULTS: A total of 300 hospitals in California in a 6-year period were included. The HF excessive readmission ratio was negatively associated with the adjusted localization index (ß=-.0474, 95% CI -0.082 to -0.013). The percentage of Black residents within the shared care areas was the only statistically significant covariate (ß=.4128, 95% CI 0.302 to 0.524). CONCLUSIONS: Higher-than-expected HF readmissions were associated with shared care networks. Control mechanisms such as the Hospital Reduction Readmission Program may need to characterize and reward shared care to guide hospitals toward a more organized HF care system.

Many heart transplant biopsies currently diagnosed as no rejection have mild molecular antibody-mediated rejection-related changes.

BACKGROUND: The Molecular Microscope (MMDx) system classifies heart transplant endomyocardial biopsies as No-rejection (NR), Early-injury, T cell-mediated (TCMR), antibody-mediated (ABMR), mixed, and possible rejection (possible TCMR, possible ABMR). Rejection-like gene expression patterns in NR biopsies have not been described. We extended the MMDx methodology, using a larger data set, to define a new "Minor" category characterized by low-level inflammation in non-rejecting biopsies. METHODS: Using MMDx criteria from a previous study, molecular rejection was assessed in 1,320 biopsies (645 patients) using microarray expression of rejection-associated transcripts (RATs). Of these biopsies, 819 were NR. A new archetypal analysis model in the 1,320 data set split the NRs into NR-Normal (N = 462) and NR-Minor (N = 359). RESULTS: Compared to NR-Normal, NR-Minor were more often histologic TCMR1R, with a higher prevalence of donor-specific antibody (DSA). DSA positivity increased in a gradient: NR-Normal 24%; NR-Minor 34%; possible ABMR 42%; ABMR 66%. The top 20 transcripts distinguishing NR-Minor from NR-Normal were all ABMR-related and/or IFNG-inducible, and also exhibited a gradient of increasing expression from NR-Normal through ABMR. In random forest analysis, TCMR and Early-injury were associated with reduced LVEF and increased graft loss, but NR-Minor and ABMR scores were not. Surprisingly, hearts with MMDx ABMR showed comparatively little graft loss. CONCLUSIONS: Many heart transplants currently diagnosed as NR by histologic or molecular assessment have minor increases in ABMR-related and IFNG-inducible transcripts, associated with DSA positivity and mild histologic inflammation. These results suggest that low-level ABMR-related molecular stress may be operating in many more hearts than previously estimated. (ClinicalTrials.gov #NCT02670408).

Assessing the Relationship Between Molecular Rejection and Parenchymal Injury in Heart Transplant Biopsies.

BACKGROUND: The INTERHEART study (ClinicalTrials.gov #NCT02670408) used genome-wide microarrays to detect rejection in endomyocardial biopsies; however, many heart transplants with no rejection have late dysfunction and impaired survival. We used the microarray measurements to develop a molecular classification of parenchymal injury. METHODS: In 1320 endomyocardial biopsies from 645 patients previously studied for rejection-associated transcripts, we measured the expression of 10 injury-induced transcript sets: 5 induced by recent injury; 2 reflecting macrophage infiltration; 2 normal heart transcript sets; and immunoglobulin transcripts, which correlate with time. We used archetypal clustering to assign injury groups. RESULTS: Injury transcript sets correlated with impaired function. Archetypal clustering based on the expression of injury transcript sets assigned each biopsy to 1 of 5 injury groups: 87 Severe-injury, 221 Late-injury, and 3 with lesser degrees of injury, 376 No-injury, 526 Mild-injury, and 110 Moderate-injury. Severe-injury had extensive loss of normal transcripts (dedifferentiation) and increase in macrophage and injury-induced transcripts. Late-injury was characterized by high immunoglobulin transcript expression. In Severe- and Late-injury, function was depressed, and short-term graft failure was increased, even in hearts with no rejection. T cell-mediated rejection almost always had parenchymal injury, and 85% had Severe- or Late-injury. In contrast, early antibody-mediated rejection (AMR) had little injury, but late AMR often had the Late-injury state. CONCLUSIONS: Characterizing heart transplants for their injury state provides new understanding of dysfunction and outcomes and demonstrates the differential impact of T cell-mediated rejection versus AMR on the parenchyma. Slow deterioration from AMR emerges as a major contributor to late dysfunction.