Regulatory T Cell Biomarkers Identify Patients at Risk of Developing Acute Cellular Rejection in the First Year Following Heart Transplantation.

BACKGROUND: Acute cellular rejection (ACR), an alloimmune response involving CD4+ and CD8+ T cells, occurs in up to 20% of patients within the first year following heart transplantation. The balance between a conventional versus regulatory CD4+ T cell alloimmune response is believed to contribute to developing ACR. Therefore, tracking these cells may elucidate whether changes in these cell populations could signal ACR risk. METHODS: We used a CD4+ T cell gene signature (TGS) panel that tracks CD4+ conventional T cells (Tconv) and regulatory T cells (Treg) on longitudinal samples from 94 adult heart transplant recipients. We evaluated combined diagnostic performance of the TGS panel with a previously developed biomarker panel for ACR diagnosis, HEARTBiT, while also investigating TGS' prognostic utility. RESULTS: Compared with nonrejection samples, rejection samples showed decreased Treg- and increased Tconv-gene expression. The TGS panel was able to discriminate between ACR and nonrejection samples and, when combined with HEARTBiT, showed improved specificity compared with either model alone. Furthermore, the increased risk of ACR in the TGS model was associated with lower expression of Treg genes in patients who later developed ACR. Reduced Treg gene expression was positively associated with younger recipient age and higher intrapatient tacrolimus variability. CONCLUSIONS: We demonstrated that expression of genes associated with CD4+ Tconv and Treg could identify patients at risk of ACR. In our post hoc analysis, complementing HEARTBiT with TGS resulted in an improved classification of ACR. Our study suggests that HEARTBiT and TGS may serve as useful tools for further research and test development.

The impact of IgG subclass deficiency on the risk of mortality in hospitalized patients with COPD.

BACKGROUND: Immunoglobulin G (IgG) deficiency increases the risk of acute exacerbations and mortality in chronic obstructive pulmonary disease (COPD). However, the impact of IgG subclass deficiency on mortality in COPD is unknown. Here, we determined which IgG subclass, if any, is associated with increased risk of mortality in COPD. METHODS: We measured serum IgG subclass concentrations of 489 hospitalized patients with COPD who were enrolled in the Rapid Transition Program (clinicaltrials.gov identifier NCT02050022). To evaluate the impact of IgG subclass deficiency on 1-year mortality, Cox proportional hazards regression analyses were performed with adjustments for potential confounders. RESULTS: Deficiencies in IgG1, IgG2, IgG3, and IgG4 were present in 1.8%, 12.1%, 4.3%, and 11.2% of patients, respectively. One-year mortality was 56% in patients with IgG1 deficiency, 27% in IgG2 deficiency, 24% in IgG3 deficiency, and 31% in IgG4 deficiency. Cox proportional modeling showed that IgG1 and IgG4 deficiencies increased the 1-year mortality risk with an adjusted hazard ratio of 3.92 (95% confidence interval [CI] = 1.55-9.87) and 1.74 (95% CI = 1.02-2.98), respectively. Neither IgG2 nor IgG3 deficiency significantly increased 1-year mortality. Two or more IgG subclass deficiencies were observed in 5.3%. Patients with 2 or more IgG subclass deficiencies had a higher 1-year mortality than those without any deficiencies (46.2% vs. 19.7%, p < 0.001), with an adjusted hazard ratio of 2.22 (95% CI = 1.18-4.17). CONCLUSIONS: IgG1 and IgG4 deficiency was observed in 1.8% and 11.2% of hospitalized patients with COPD, respectively, and these deficiencies were associated with a significantly increased risk of 1-year mortality.

Automated medical chart review for breast cancer outcomes research: a novel natural language processing extraction system.

BACKGROUND: Manually extracted data points from health records are collated on an institutional, provincial, and national level to facilitate clinical research. However, the labour-intensive clinical chart review process puts an increasing burden on healthcare system budgets. Therefore, an automated information extraction system is needed to ensure the timeliness and scalability of research data. METHODS: We used a dataset of 100 synoptic operative and 100 pathology reports, evenly split into 50 reports in training and test sets for each report type. The training set guided our development of a Natural Language Processing (NLP) extraction pipeline system, which accepts scanned images of operative and pathology reports. The system uses a combination of rule-based and transfer learning methods to extract numeric encodings from text. We also developed visualization tools to compare the manual and automated extractions. The code for this paper was made available on GitHub. RESULTS: A test set of 50 operative and 50 pathology reports were used to evaluate the extraction accuracies of the NLP pipeline. Gold standard, defined as manual extraction by expert reviewers, yielded accuracies of 90.5% for operative reports and 96.0% for pathology reports, while the NLP system achieved overall 91.9% (operative) and 95.4% (pathology) accuracy. The pipeline successfully extracted outcomes data pertinent to breast cancer tumor characteristics (e.g. presence of invasive carcinoma, size, histologic type), prognostic factors (e.g. number of lymph nodes with micro-metastases and macro-metastases, pathologic stage), and treatment-related variables (e.g. margins, neo-adjuvant treatment, surgical indication) with high accuracy. Out of the 48 variables across operative and pathology codebooks, NLP yielded 43 variables with F-scores of at least 0.90; in comparison, a trained human annotator yielded 44 variables with F-scores of at least 0.90. CONCLUSIONS: The NLP system achieves near-human-level accuracy in both operative and pathology reports using a minimal curated dataset. This system uniquely provides a robust solution for transparent, adaptable, and scalable automation of data extraction from patient health records. It may serve to advance breast cancer clinical research by facilitating collection of vast amounts of valuable health data at a population level.

Epigenetic marker of telomeric age is associated with exacerbations and hospitalizations in chronic obstructive pulmonary disease.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is an age-related condition that has been associated with early telomere attrition; the clinical implications of telomere shortening in COPD are not well known. In this study we aimed to determine the relationship of the epigenetic regulation of telomeric length in peripheral blood with the risk of exacerbations and hospitalization in patients with COPD. METHODS: Blood DNA methylation profiles were obtained from 292 patients with COPD enrolled in the placebo arm of the Macrolide Azithromycin to Prevent Rapid Worsening of Symptoms Associated with Chronic Obstructive Pulmonary Disease (MACRO) Study and who were followed for 1-year. We calculated telomere length based on DNA methylation markers (DNAmTL) and related this biomarker to the risk of exacerbation and hospitalization and health status (St. George Respiratory Questionnaire [SGRQ]) score over time using a Cox proportional hazards model. We also used linear models to investigate the associations of DNAmTL with the rates of exacerbation and hospitalization (adjusted for chronological age, lung function, race, sex, smoking, body mass index and cell composition). RESULTS: Participants with short DNAmTL demonstrated increased risk of exacerbation (P = 0.02) and hospitalization (P = 0.03) compared to those with longer DNAmTL. DNAmTL age acceleration was associated with higher rates of exacerbation (P = 1.35 × 10-04) and hospitalization (P = 5.21 × 10-03) and poor health status (lower SGRQ scores) independent of chronological age (P = 0.03). CONCLUSION: Telomeric age based on blood DNA methylation is associated with COPD exacerbation and hospitalization and thus a promising biomarker for poor outcomes in COPD.

Aortic Dimensions, Biophysical Properties, and Plasma Biomarkers in Children and Adults with Marfan or Loeys-Dietz Syndrome.

BACKGROUND: Aortic dilation, stiffening, and dissection are common and potentially lethal complications of Marfan syndrome (MFS) and Loeys-Dietz syndrome (LDS), which involve abnormal transforming growth factor beta (TGF-ß) signalling. The relation of aortic dimensions, stiffness, and biomarker levels is unknown. The objective of this study was to measure aortic dimensions, stiffness, TGF-ß and matrix metalloproteinase (MMP) levels, and endothelial function in patients with MFS, and to compare TGF-ß levels in patients with MFS receiving different therapeutic regimens. METHODS: This was a cohort study of 40 MFS and 4 LDS patients and 87 control participants. Aortic dimension and stiffness indexes, including pulse wave velocity (PWV), were measured using echocardiography and Doppler. Total and free TGF-ß and MMP blood levels were measured using Quantikine (R&D Systems, Inc, Minneapolis, MN) and Quanterix (Billerica, MA) kits. Endothelial function was measured using brachial artery flow-mediated dilation. RESULTS: PWV was increased in patients with MFS. There were increased MMP-2 levels in those with MFS but no increase in free or total TGF-ß or MMP-9 levels compared with control participants. There was no difference in TGF-ß levels between MFS patients receiving no medications, angiotensin receptor blockers, and ß-blockers. PWV correlated most strongly with age. Endothelial function showed premature gradual decline in patients with MFS. CONCLUSIONS: Despite the increased PWV, monitoring aortic stiffness or TGF-ß levels would not be helpful in patients with MFS. TGF-ß levels were not increased and the increased MMP-2 levels suggest consideration of a different therapeutic target.

CONTEXTE: La dilatation, la rigidification et la dissection de l'aorte sont des complications fréquentes et parfois mortelles du syndrome de Marfan (SM) et du syndrome de Loeys-Dietz (SLD), qui sont tous deux dûs à une anomalie de la voie de signalisation du facteur de croissance transformant bêta (TGF-ß). On ne connaît pas la relation entre les dimensions et la rigidité de l'aorte et la présence de biomarqueurs. Notre étude visait à mesurer les dimensions et la rigidité de l'aorte, les taux de TGF-ß et de métalloprotéases matricielles (MMP) et la fonction endothéliale chez des patients atteints du SM, et à les comparer aux taux de TGF-ß observés chez des patients également atteints de SM, mais recevant un autre traitement. MÉTHODOLOGIE: Il s'agissait d'une étude de cohorte menée auprès de 40 patients atteints du SM et de quatre patients atteints du SLD, ainsi que de 87 témoins. Les indices des dimensions et de la rigidité aortiques, y compris la vitesse d'onde de pouls (VOP), ont été mesurés par échocardiographie et par échographie Doppler. Les taux sanguins de TGF-ß et de MMP totaux et libres ont été mesurés à l'aide de trousses Quantikine (R&D Systems, Inc, Minneapolis, MN) et Quanterix (Billerica, MA). La fonction endothéliale a été mesurée par dilatation liée au flux dans l'artère brachiale. RÉSULTATS: La VOP était plus élevée chez les patients atteints du SM. On a aussi observé une hausse des taux de MMP-2 chez les patients atteints de SM, mais aucune augmentation des taux de TGF-ß ou de MMP-9 libres ou totaux comparativement aux témoins. Il n'y avait pas de différence entre les taux de TGF-ß chez les patients atteints de SM ne recevant aucun traitement, ceux qui prenaient un antagoniste des récepteurs de l'angiotensine et ceux qui prenaient un bêtabloquant. La VOP été plus fortement corrélée avec l'âge. La fonction endothéliale a affiché un déclin progressif prématuré chez les patients atteints du SM. CONCLUSIONS: Malgré l'augmentation de la VOP, il ne semble pas utile de surveiller la rigidité aortique ni les taux de TGF-ß en cas de SM. Les taux de TGF-ß n'étaient pas plus élevés chez les patients atteints du SM, et la hausse des taux de MMP-2 indique qu'il conviendrait de choisir une autre cible thérapeutique.

Analytical Validation of HEARTBiT: A Blood-Based Multiplex Gene Expression Profiling Assay for Exclusionary Diagnosis of Acute Cellular Rejection in Heart Transplant Patients.

BACKGROUND: HEARTBiT is a whole blood-based gene profiling assay using the nucleic acid counting NanoString technology for the exclusionary diagnosis of acute cellular rejection in heart transplant patients. The HEARTBiT score measures the risk of acute cellular rejection in the first year following heart transplant, distinguishing patients with stable grafts from those at risk for acute cellular rejection. Here, we provide the analytical performance characteristics of the HEARTBiT assay and the results on pilot clinical validation. METHODS: We used purified RNA collected from PAXgene blood samples to evaluate the characteristics of a 12-gene panel HEARTBiT assay, for its linearity range, quantitative bias, precision, and reproducibility. These parameters were estimated either from serial dilutions of individual samples or from repeated runs on pooled samples. RESULTS: We found that all 12 genes showed linear behavior within the recommended assay input range of 125 ng to 500 ng of purified RNA, with most genes showing 3% or lower quantitative bias and around 5% coefficient of variation. Total variation resulting from unique operators, reagent lots, and runs was less than 0.02 units standard deviation (SD). The performance of the analytically validated assay (AUC = 0.75) was equivalent to what we observed in the signature development dataset. CONCLUSION: The analytical performance of the assay within the specification input range demonstrated reliable quantification of the HEARTBiT score within 0.02 SD units, measured on a 0 to 1 unit scale. This assay may therefore be of high utility in clinical validation of HEARTBiT in future biomarker observational trials.

HEARTBiT: A Transcriptomic Signature for Excluding Acute Cellular Rejection in Adult Heart Allograft Patients.

BACKGROUND: Nine mRNA transcripts associated with acute cellular rejection (ACR) in previous microarray studies were ported to the clinically amenable NanoString nCounter platform. Here we report the diagnostic performance of the resulting blood test to exclude ACR in heart allograft recipients: HEARTBiT. METHODS: Blood samples for transcriptomic profiling were collected during routine post-transplantation monitoring in 8 Canadian transplant centres participating in the Biomarkers in Transplantation initiative, a large (n = 1622) prospective observational study conducted between 2009 and 2014. All adult cardiac transplant patients were invited to participate (median age = 56 [17 to 71]). The reference standard for rejection status was histopathology grading of tissue from endomyocardial biopsy (EMB). All locally graded ISHLT ≥ 2R rejection samples were selected for analysis (n = 36). ISHLT 1R (n = 38) and 0R (n = 86) samples were randomly selected to create a cohort approximately matched for site, age, sex, and days post-transplantation, with a focus on early time points (median days post-transplant = 42 [7 to 506]). RESULTS: ISHLT ≥ 2R rejection was confirmed by EMB in 18 and excluded in 92 samples in the test set. HEARTBiT achieved 47% specificity (95% confidence interval [CI], 36%-57%) given ≥ 90% sensitivity, with a corresponding area under the receiver operating characteristic curve of 0.69 (95% CI, 0.56-0.81). CONCLUSIONS: HEARTBiT's diagnostic performance compares favourably to the only currently approved minimally invasive diagnostic test to rule out ACR, AlloMap (CareDx, Brisbane, CA) and may be used to inform care decisions in the first 2 months post-transplantation, when AlloMap is not approved, and most ACR episodes occur.

Blood biomarkers to predict short-term pulmonary exacerbation risk in children and adolescents with CF: A pilot study.

In CF, pulmonary exacerbations (PEx) can lead to permanent loss in lung function and thus should be prevented. Previously, we identified a blood protein biosignature consisting of 6 proteins capable of predicting short-term PEx events in CF adults. In this study, we utilized blood samples from the placebo arm of a randomized controlled trial to assess whether this candidate protein biosignature was also capable of predicting short-term PEx events in CF children and adolescents. This pilot study provides preliminary evidence that blood inflammation can be monitored to predict short-term PEx risk in CF children and adolescents.

Effect of short-term oral prednisone therapy on blood gene expression: a randomised controlled clinical trial.

BACKGROUND: Effects of systemic corticosteroids on blood gene expression are largely unknown. This study determined gene expression signature associated with short-term oral prednisone therapy in patients with chronic obstructive pulmonary disease (COPD) and its relationship to 1-year mortality following an acute exacerbation of COPD (AECOPD). METHODS: Gene expression in whole blood was profiled using the Affymetrix Human Gene 1.1 ST microarray chips from two cohorts: 1) a prednisone cohort with 37 stable COPD patients randomly assigned to prednisone 30 mg/d + standard therapy for 4 days or standard therapy alone and 2) the Rapid Transition Program (RTP) cohort with 218 COPD patients who experienced AECOPD and were treated with systemic corticosteroids. All gene expression data were adjusted for the total number of white blood cells and their differential cell counts. RESULTS: In the prednisone cohort, 51 genes were differentially expressed between prednisone and standard therapy group at a false discovery rate of < 0.05. The top 3 genes with the largest fold-changes were KLRF1, GZMH and ADGRG1; and 21 genes were significantly enriched in immune system pathways including the natural killer cell mediated cytotoxicity. In the RTP cohort, 27 patients (12.4%) died within 1 year after hospitalisation of AECOPD; 32 of 51 genes differentially expressed in the prednisone cohort significantly changed from AECOPD to the convalescent state and were enriched in similar cellular immune pathways to that in the prednisone cohort. Of these, 10 genes including CX3CR1, KLRD1, S1PR5 and PRF1 were significantly associated with 1-year mortality. CONCLUSIONS: Short-term daily prednisone therapy produces a distinct blood gene signature that may be used to determine and monitor treatment responses to prednisone in COPD patients during AECOPD. TRIAL REGISTRATION: The prednisone cohort was registered at clinicalTrials.gov ( NCT02534402 ) and the RTP cohort was registered at ClinicalTrials.gov ( NCT02050022 ).

Ensembling Electrical and Proteogenomics Biomarkers for Improved Prediction of Cardiac-Related 3-Month Hospitalizations: A Pilot Study.

BACKGROUND: Many risk models for predicting mortality, hospitalizations, or both in patients with heart failure have been developed but do not have sufficient discriminatory ability. The purpose of this study was to identify predictive biomarkers of hospitalizations in heart failure patients using omics-based technologies applied to blood and electrical monitoring of the heart. METHODS: Blood samples were collected from 58 heart failure patients during enrollment into this study. Each patient wore a 48-hour Holter monitor that recorded the electrical activity of their heart. The blood samples were profiled for gene expression using microarrays and protein levels using multiple reaction monitoring. Statistical deconvolution was used to estimate cellular frequencies of common blood cells. Classification models were developed using clinical variables, Holter variables, cell types, gene transcripts, and proteins to predict hospitalization status. RESULTS: Of the 58 patients recruited, 13 were hospitalized within 3 months after enrollment. These patients had lower diastolic and systolic blood pressures, higher brain natriuretic peptide levels, most had higher blood creatinine levels, and had been diagnosed with heart failure for a longer time period. The best-performing clinical model had an area under the receiver operating characteristic curve of 0.76. An ensemble biomarker panel consisting of Holter variables, cell types, gene transcripts, and proteins had an area under the receiver operating characteristic curve of 0.88. CONCLUSIONS: Molecular-based analyses as well as sensory data might provide sensitive biomarkers for the prediction of hospitalizations in heart failure patients. These approaches may be combined with traditional clinical models for the development of improved risk prediction models for heart failure.

Phenotyping and outcomes of hospitalized COPD patients using rapid molecular diagnostics on sputum samples.

BACKGROUND: Etiologies of acute exacerbations of chronic obstructive pulmonary disease (AECOPD) are heterogeneous. We phenotyped severe AECOPD based on molecular pathogen detection of sputum samples collected at hospitalization of COPD patients and determined their outcomes. METHODS: We phenotyped 72 sputum samples of COPD patients who were hospitalized with a primary diagnosis of AECOPD using a molecular array that detected common bacterial and viral respiratory pathogens. Based on these results, the patients were classified into positive or negative pathogen groups. The pathogen-positive group was further divided into virus or bacteria subgroups. Admission day 1 blood samples were assayed for N-terminal prohormone brain natriuretic peptide, CRP, and complete blood counts. RESULTS: A total of 52 patients had a positive result on the array, while 20 patients had no pathogens detected. The most common bacterial pathogen detected was Haemophilus influenzae and the most common virus was rhinovirus. The pathogen-negative group had the worse outcomes with longer hospital stays (median 6.5 vs 5 days for bacteria-positive group, P=0.02) and a trend toward increased 1-year mortality (P=0.052). The bacteria-positive group had the best prognosis, whereas the virus-positive group had outcomes somewhere in between the bacteria-positive and pathogen-negative groups. CONCLUSION: Molecular diagnostics on sputum can rapidly phenotype serious AECOPD into bacteria-, virus-, or pathogen-negative groups. The bacteria-positive group appears to have the best prognosis, while pathogen-negative group has the worst. These data suggest that AECOPD is a heterogeneous event and that accurate phenotyping of AECOPD may lead to novel management strategies that are personalized and more precise.

Sputum Microbiome Is Associated with 1-Year Mortality after Chronic Obstructive Pulmonary Disease Hospitalizations.

Rationale: Lung dysbiosis promotes airway inflammation and decreased local immunity, potentially playing a role in the pathogenesis of acute exacerbations of chronic obstructive pulmonary disease (AECOPD). Objectives: We sought to determine the relationship between sputum microbiome at the time of AECOPD hospitalization and 1-year mortality in a COPD cohort. Methods: We used sputum samples from 102 patients hospitalized because of AECOPD. All subjects were followed for 1 year after discharge. The microbiome profile was assessed through sequencing of 16S rRNA gene. Microbiome analyses were performed according to 1-year mortality status. To investigate the effect of α-diversity measures and taxon features on time to death, we applied Cox proportional hazards regression models and obtained hazard ratios (HRs) associated with these variables. Measurements and Main Results: We observed significantly lower values of α-diversity (richness, Shannon index, evenness, and Faith's Phylogenetic Diversity) among nonsurvivors (n = 19, 18.6%) than survivors (n = 83, 81.4%). ß-Diversity analysis also demonstrated significant differences between both groups (adjusted permutational multivariate ANOVA, P = 0.010). The survivors had a higher relative abundance of Veillonella; in contrast, nonsurvivors had a higher abundance of Staphylococcus. The adjusted HRs for 1-year mortality increased significantly with decreasing α-diversity. We also observed lower survival among patients in whom sputum samples were negative for Veillonella (HR, 13.5; 95% confidence interval, 4.2-43.9; P < 0.001) or positive for Staphylococcus (HR, 7.3; 95% confidence interval, 1.6-33.2; P = 0.01). Conclusions: The microbiome profile of sputum in AECOPD is associated with 1-year mortality and may be used to identify subjects with a poor prognosis at the time of hospitalization.

Development and Validation of Apolipoprotein AI-Associated Lipoprotein Proteome Panel for the Prediction of Cholesterol Efflux Capacity and Coronary Artery Disease.

BACKGROUND: Cholesterol efflux capacity (CEC) is a measure of HDL function that, in cell-based studies, has demonstrated an inverse association with cardiovascular disease. The cell-based measure of CEC is complex and low-throughput. We hypothesized that assessment of the lipoprotein proteome would allow for precise, high-throughput CEC prediction. METHODS: After isolating lipoprotein particles from serum, we used LC-MS/MS to quantify 21 lipoprotein-associated proteins. A bioinformatic pipeline was used to identify proteins with univariate correlation to cell-based CEC measurements and generate a multivariate algorithm for CEC prediction (pCE). Using logistic regression, protein coefficients in the pCE model were reweighted to yield a new algorithm predicting coronary artery disease (pCAD). RESULTS: Discovery using targeted LC-MS/MS analysis of 105 training and test samples yielded a pCE model comprising 5 proteins (Spearman r = 0.86). Evaluation of pCE in a case-control study of 231 specimens from healthy individuals and patients with coronary artery disease revealed lower pCE in cases (P = 0.03). Derived within this same study, the pCAD model significantly improved classification (P < 0.0001). Following analytical validation of the multiplexed proteomic method, we conducted a case-control study of myocardial infarction in 137 postmenopausal women that confirmed significant separation of specimen cohorts in both the pCE (P = 0.015) and pCAD (P = 0.001) models. CONCLUSIONS: Development of a proteomic pCE provides a reproducible high-throughput alternative to traditional cell-based CEC assays. The pCAD model improves stratification of case and control cohorts and, with further studies to establish clinical validity, presents a new opportunity for the assessment of cardiovascular health.

Phenotyping COPD exacerbations using imaging and blood-based biomarkers.

Rationale: Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) are caused by a variety of different etiologic agents. Our aim was to phenotype COPD exacerbations using imaging (chest X-ray [CXR] and computed tomography [CT]) and to determine the possible role of the blood tests (C-reactive protein [CRP], the N-terminal prohormone brain natriuretic peptide [NT-proBNP]) as diagnostic biomarkers. Materials and methods: Subjects who were hospitalized with a primary diagnosis of AECOPD and who had had CXRs, CT scans, and blood collection for CRP and NT-proBNP were assessed in this study. Radiologist blinded to the clinical and laboratory characteristics of the subjects interpreted their CXRs and CT images. ANOVA and Spearman's correlation were performed to test for associations between these imaging parameters and the blood-based biomarkers NT-proBNP and CRP; logistic regression models were used to assess the performance of these biomarkers in predicting the radiological parameters. Results: A total of 309 subjects were examined for this study. Subjects had a mean age of 65.6±11.1 years, 66.7% of them were males, and 62.4% were current smokers, with a mean FEV1 54.4%±21.5% of predicted. Blood NT-proBNP concentrations were associated with cardiac enlargement (area under the curve [AUC] =0.72, P<0.001), pulmonary edema (AUC =0.63, P=0.009), and pleural effusion on CXR (AUC =0.64, P=0.01); whereas on CT images, NT-proBNP concentrations were associated with pleural effusion (AUC =0.71, P=0.002). Serum CRP concentrations, on the other hand, were associated with consolidation on CT images (AUC =0.75, P<0.001), ground glass opacities (AUC =0.64, P=0.028), and pleural effusion (AUC =0.72, P<0.001) on CT images. A serum CRP sensitivity-oriented cutoff point of 11.5 mg/L was selected for the presence of consolidation on CT images in subjects admitted as cases of AECOPD, which has a sensitivity of 91% and a specificity of 53% (P<0.001). Conclusion: Elevated CRP may indicate the presence of pneumonia, while elevated NT-proBNP may indicate cardiac dysfunction. These readily available blood-based biomarkers may provide more accurate phenotyping of AECOPD and enable the discovery of more precise therapies.

Multiple reaction monitoring mass spectrometry to identify novel plasma protein biomarkers of treatment response in cystic fibrosis pulmonary exacerbations.

BACKGROUND: Systemic inflammation decreases with IV antibiotics during the treatment of CF pulmonary exacerbations (PEx). We used multiple reaction monitoring mass spectrometry and immunoassays to monitor blood proteins during PEx treatment to determine if early changes could be used to predict PEx outcomes following treatment. METHODS: Blood samples from 25 PEx (22 unique adults) were collected within 24h of admission, day 5, day 10, and at IV antibiotic completion. Ninety-two blood proteins involved in host immunity and inflammation were measured. RESULTS: Levels of several blood proteins changed from admission to end of IV antibiotics, most increasing with treatment. Early changes (admission to day 5) in fibrinogen levels had the strongest correlation with overall improvement in CFRSD-CRISS and FEV1% predicted by the end of treatment. CONCLUSIONS: Several plasma proteins changed significantly with IV antibiotics. Future studies will evaluate fibrinogen as an early biomarker of PEx treatment response in CF.

Thrombospondin-1 and disease progression in dysferlinopathy.

The purpose of this study was to determine whether thrombospondin (TSP)-1 promotes macrophage activity and disease progression in dysferlinopathy. First, we found that levels of TSP-1 are elevated in blood of non-ambulant dysferlinopathy patients compared with ambulant patients and healthy controls, supporting the idea that TSP-1 levels are correlated with disease progression. We then crossed dysferlinopathic BlaJ mice with TSP-1 knockout mice and assessed disease progression longitudinally with magnetic resonance imaging (MRI). In these mice, deletion of TSP-1 ameliorated loss in volume and mass of the moderately affected gluteal muscle but not of the severely affected psoas muscle. T2 MRI parameters revealed that loss of TSP-1 modestly inhibited inflammation only in gluteal muscle of male mice. Histological assessment indicated that deletion of TSP-1 reduced inflammatory cell infiltration of muscle fibers, but only early in disease progression. In addition, flow cytometry analysis revealed that, in males, TSP-1 knockout reduced macrophage infiltration and phagocytic activity, which is consistent with TSP-1-enhanced phagocytosis and pro-inflammatory cytokine induction in cultured macrophages. In summary, TSP-1 appears to play an accessory role in modulating Mp activity in BlaJ mice in a gender, age and muscle-dependent manner, but is unlikely a primary driver of disease progression of dysferlinopathy.

Differentiating heart failure phenotypes using sex-specific transcriptomic and proteomic biomarker panels.

AIMS: Heart failure with preserved ejection fraction (HFpEF) accounts for 30-50% of patients with heart failure (HF). A major obstacle in HF management is the difficulty in differentiating between HFpEF and heart failure with reduced ejection fraction (HFrEF) using conventional clinical and laboratory investigations. The aim of this study is to develop robust transcriptomic and proteomic biomarker signatures that can differentiate HFpEF from HFrEF. METHODS AND RESULTS: A total of 210 HF patients were recruited in participating institutions from the Alberta HEART study. An expert clinical adjudicating panel differentiated between patients with HFpEF and HFrEF. The discovery cohort consisted of 61 patients, and the replication cohort consisted of 70 patients. Transcriptomic and proteomic data were analysed to find panels of differentiating HFpEF from HFrEF. In the discovery cohort, a 22-transcript panel was found to differentiate HFpEF from HFrEF in male patients with a cross-validation AUC of 0.74, as compared with 0.70 for N-terminal pro-B-type natriuretic peptide (NT-proBNP) in those same patients. An ensemble of the transcript panel and NT-pro-BNP yielded a cross-validation AUC of 0.80. This performance improvement was also observed in the replication cohort. An ensemble of the transcriptomic panel with NT-proBNP produced a replication AUC of 0.90, as compared with 0.74 for NT-proBNP alone and 0.73 for the transcriptomic panel. CONCLUSIONS: We have identified a male-specific transcriptomic biomarker panel that can differentiate between HFpEF and HFrEF. These biosignatures could be further replicated on other patients and potentially be developed into a blood test for better management of HF patients.