Microbial colonization programs are structured by breastfeeding and guide healthy respiratory development.

Breastfeeding and microbial colonization during infancy occur within a critical time window for development, and both are thought to influence the risk of respiratory illness. However, the mechanisms underlying the protective effects of breastfeeding and the regulation of microbial colonization are poorly understood. Here, we profiled the nasal and gut microbiomes, breastfeeding characteristics, and maternal milk composition of 2,227 children from the CHILD Cohort Study. We identified robust colonization patterns that, together with milk components, predict preschool asthma and mediate the protective effects of breastfeeding. We found that early cessation of breastfeeding (before 3 months) leads to the premature acquisition of microbial species and functions, including Ruminococcus gnavus and tryptophan biosynthesis, which were previously linked to immune modulation and asthma. Conversely, longer exclusive breastfeeding supports a paced microbial development, protecting against asthma. These findings underscore the importance of extended breastfeeding for respiratory health and highlight potential microbial targets for intervention.

Antibiotics taken within the first year of life are linked to infant gut microbiome disruption and elevated atopic dermatitis risk.

BACKGROUND: Atopic dermatitis (AD) is the most common chronic inflammatory skin disease in both pediatric and adult populations. The development of AD has been linked to antibiotic usage, which causes perturbation of the microbiome and has been associated with abnormal immune system function. However, imbalances in the gut microbiome itself associated with antibiotic usage have been inconsistently linked to AD. OBJECTIVES: This study aimed to elucidate the timing and specific factors mediating the relationship between systemic (oral or intravenous) antibiotic usage and AD. METHODS: We used statistical modeling and differential analysis to link CHILD Cohort Study participants' history of antibiotic usage and early-life gut microbiome alterations to AD. RESULTS: Here we report that systemic antibiotics during the first year of life, as compared to later, are associated with AD risk (adjusted odds ratio [aOR] = 1.81; 95% CI: 1.28-2.57; P < .001), with an increased number of antibiotic courses corresponding to a dose response-like increased risk of AD risk (1 course: aOR: 1.67; 95% CI: 1.17-2.38; 2 or more courses: aOR: 2.16; 95% CI: 1.30-3.59). Further, we demonstrate that microbiome alterations associated with both AD and systemic antibiotic usage fully mediate the effect of antibiotic usage on the development of AD (ßindirect = 0.072; P < .001). Alterations in the 1-year infant gut microbiome of participants who would later develop AD included increased Tyzzerella nexilis, increased monosaccharide utilization, and parallel decreased Bifidobacterium and Eubacterium spp, and fermentative pathways. CONCLUSIONS: These findings indicate that early-life antibiotic usage, especially in the first year of life, modulates key gut microbiome components that may be used as markers to predict and possibly prevent the development of AD.

Early prediction of pediatric asthma in the Canadian Healthy Infant Longitudinal Development (CHILD) birth cohort using machine learning.

BACKGROUND: Early identification of children at risk of asthma can have significant clinical implications for effective intervention and treatment. This study aims to disentangle the relative timing and importance of early markers of asthma. METHODS: Using the CHILD Cohort Study, 132 variables measured in 1754 multi-ethnic children were included in the analysis for asthma prediction. Data up to 4 years of age was used in multiple machine learning models to predict physician-diagnosed asthma at age 5 years. Both predictive performance and variable importance was assessed in these models. RESULTS: Early-life data (≤1 year) has limited predictive ability for physician-diagnosed asthma at age 5 years (area under the precision-recall curve (AUPRC) < 0.35). The earliest reliable prediction of asthma is achieved at age 3 years, (area under the receiver-operator curve (AUROC) > 0.90) and (AUPRC > 0.80). Maternal asthma, antibiotic exposure, and lower respiratory tract infections remained highly predictive throughout childhood. Wheezing status and atopy are the most important predictors of early childhood asthma from among the factors included in this study. CONCLUSIONS: Childhood asthma is predictable from non-biological measurements from the age of 3 years, primarily using parental asthma and patient history of wheezing, atopy, antibiotic exposure, and lower respiratory tract infections. IMPACT: Machine learning models can predict physician-diagnosed asthma in early childhood (AUROC > 0.90 and AUPRC > 0.80) using ≥3 years of non-biological and non-genetic information, whereas prediction with the same patient information available before 1 year of age is challenging. Wheezing, atopy, antibiotic exposure, lower respiratory tract infections, and the child's mother having asthma were the strongest early markers of 5-year asthma diagnosis, suggesting an opportunity for earlier diagnosis and intervention and focused assessment of patients at risk for asthma, with an evolving risk stratification over time.

Delayed gut microbiota maturation in the first year of life is a hallmark of pediatric allergic disease.

Allergic diseases affect millions of people worldwide. An increase in their prevalence has been associated with alterations in the gut microbiome, i.e., the microorganisms and their genes within the gastrointestinal tract. Maturation of the infant immune system and gut microbiota occur in parallel; thus, the conformation of the microbiome may determine if tolerant immune programming arises within the infant. Here we show, using deeply phenotyped participants in the CHILD birth cohort (n = 1115), that there are early-life influences and microbiome features which are uniformly associated with four distinct allergic diagnoses at 5 years: atopic dermatitis (AD, n = 367), asthma (As, n = 165), food allergy (FA, n = 136), and allergic rhinitis (AR, n = 187). In a subset with shotgun metagenomic and metabolomic profiling (n = 589), we discover that impaired 1-year microbiota maturation may be universal to pediatric allergies (AD p = 0.000014; As p = 0.0073; FA p = 0.00083; and AR p = 0.0021). Extending this, we find a core set of functional and metabolic imbalances characterized by compromised mucous integrity, elevated oxidative activity, decreased secondary fermentation, and elevated trace amines, to be a significant mediator between microbiota maturation at age 1 year and allergic diagnoses at age 5 years (ßindirect = -2.28; p = 0.0020). Microbiota maturation thus provides a focal point to identify deviations from normative development to predict and prevent allergic disease.

Breastfeeding enrichment of B. longum subsp. infantis mitigates the effect of antibiotics on the microbiota and childhood asthma risk.

BACKGROUND: Early antibiotic exposure is linked to persistent disruption of the infant gut microbiome and subsequent elevated pediatric asthma risk. Breastfeeding acts as a primary modulator of the gut microbiome during early life, but its effect on asthma development has remained unclear. METHODS: We harnessed the CHILD cohort to interrogate the influence of breastfeeding on antibiotic-associated asthma risk in a subset of children (n = 2,521). We then profiled the infant microbiomes in a subset of these children (n = 1,338) using shotgun metagenomic sequencing and compared human milk oligosaccharide and fatty acid composition from paired maternal human milk samples for 561 of these infants. FINDINGS: Children who took antibiotics without breastfeeding had 3-fold higher asthma odds, whereas there was no such association in children who received antibiotics while breastfeeding. This benefit was associated with widespread "re-balancing" of taxonomic and functional components of the infant microbiome. Functional changes associated with asthma protection were linked to enriched Bifidobacterium longum subsp. infantis colonization. Network analysis identified a selection of fucosylated human milk oligosaccharides in paired maternal samples that were positively associated with B. infantis and these broader functional changes. CONCLUSIONS: Our data suggest that breastfeeding and antibiotics have opposing effects on the infant microbiome and that breastfeeding enrichment of B. infantis is associated with reduced antibiotic-associated asthma risk. FUNDING: This work was supported in part by the Canadian Institutes of Health Research; the Allergy, Genes and Environment Network of Centres of Excellence; Genome Canada; and Genome British Columbia.

Human JAK1 gain of function causes dysregulated myelopoeisis and severe allergic inflammation.

Primary atopic disorders are a group of inborn errors of immunity that skew the immune system toward severe allergic disease. Defining the biology underlying these extreme monogenic phenotypes reveals shared mechanisms underlying common polygenic allergic disease and identifies potential drug targets. Germline gain-of-function (GOF) variants in JAK1 are a cause of severe atopy and eosinophilia. Modeling the JAK1GOF (p.A634D) variant in both zebrafish and human induced pluripotent stem cells (iPSCs) revealed enhanced myelopoiesis. RNA-Seq of JAK1GOF human whole blood, iPSCs, and transgenic zebrafish revealed a shared core set of dysregulated genes involved in IL-4, IL-13, and IFN signaling. Immunophenotypic and transcriptomic analysis of patients carrying a JAK1GOF variant revealed marked Th cell skewing. Moreover, long-term ruxolitinib treatment of 2 children carrying the JAK1GOF (p.A634D) variant remarkably improved their growth, eosinophilia, and clinical features of allergic inflammation. This work highlights the role of JAK1 signaling in atopic immune dysregulation and the clinical impact of JAK1/2 inhibition in treating eosinophilic and allergic disease.

Longitudinal body mass index trajectories at preschool age: children with rapid growth have differential composition of the gut microbiota in the first year of life.

BACKGROUND/OBJECTIVE: The steep rise in childhood obesity has emerged as a worldwide public health problem. The first 4 years of life are a critical window where long-term developmental patterns of body mass index (BMI) are established and a critical period for microbiota maturation. Understanding how the early-life microbiota relate to preschool growth may be useful for identifying preventive interventions for childhood obesity. We aim to investigate whether longitudinal shifts within the bacterial community between 3 months and 1 year of life are associated with preschool BMI z-score trajectories. METHODS: BMI trajectories from birth to 5 years of age were identified using group-based trajectory modeling in 3059 children. Their association with familial and environmental factors were analyzed. Infant gut microbiota at 3 months and 1 year was defined by 16S RNA sequencing and changes in diversity and composition within each BMIz trajectory were analyzed. RESULTS: Four BMIz trajectories were identified: low stable, normative, high stable, and rapid growth. Infants in the rapid growth trajectory were less likely to have been breastfed, and gained less microbiota diversity in the first year of life. Relative abundance of Akkermansia increased with age in children with stable growth, but decreased in those with rapid growth, abundance of Ruminococcus and Clostridium at 1 year were elevated in children with rapid growth. Children who were breastfed at 6 months had increased levels of Sutterella, and decreased levels of Ruminococcus and Clostridium. CONCLUSION: This study provides new insights into the relationship between the gut microbiota in infancy and patterns of growth in a cohort of preschool Canadian children. We highlight that rapid growth since birth is associated with bacteria shown in animal models to have a causative role in weight gain. Our findings support a novel avenue of research targeted on tangible interventions to reduce childhood obesity.

Inborn Errors of Immunity Associated With Type 2 Inflammation in the USIDNET Registry.

Background: Monogenic conditions that disrupt proper development and/or function of the immune system are termed inborn errors of immunity (IEIs), also known as primary immunodeficiencies. Patients with IEIs often suffer from other manifestations in addition to infection, and allergic inflammation is an increasingly recognized feature of these conditions. Methods: We performed a retrospective analysis of IEIs presenting with allergic inflammation as reported in the USIDNET registry. Our inclusion criteria comprised of patients with a reported monogenic cause for IEI where reported lab eosinophil and/or IgE values were available for the patient prior to them receiving potentially curative therapy. Patients were excluded if we were unable to determine the defective gene underlying their IEI. Patients were classified as having eosinophilia or elevated IgE when their record included at least 1 eosinophil count or IgE value that was greater than the age stratified upper limit of normal. We compared the proportion of patients with eosinophilia or elevated IgE with the proportion of samples in a reference population that fall above the upper limit of normal (2.5%). Results: The query submitted to the USIDNET registry identified 1409 patients meeting inclusion criteria with a monogenic cause for their IEI diagnosis, of which 975 had eosinophil counts and 645 had IgE levels obtained prior to transplantation or gene therapy that were available for analysis. Overall, 18.8% (183/975) of the patients evaluated from the USIDNET registry had eosinophilia and 20.9% (135/645) had an elevated IgE. IEIs caused by defects in 32 genes were found to be significantly associated with eosinophilia and/or an elevated IgE level, spanning 7 of the 10 IEI categories according to the International Union of Immunological Societies classification. Conclusion: Type 2 inflammation manifesting as eosinophilia or elevated IgE is found in a broad range of IEIs in the USIDNET registry. Our findings suggest that allergic immune dysregulation may be more widespread in IEIs than previously reported.

Early-life cytomegalovirus infection is associated with gut microbiota perturbations and increased risk of atopy.

BACKGROUND: The "old friends" hypothesis posits that reduced exposure to previously ubiquitous microorganisms is one factor involved in the increased rates of allergic diseases. Cytomegalovirus (CMV) may be one of the "old friends" hypothesized to help prevent allergic diseases. We sought to elucidate whether early-life CMV infection is associated with childhood atopy via perturbations of the gut microbiota. METHODS: Participants were recruited from a population-based birth cohort (CHILD study) and followed prospectively until age 5 years in four Canadian cities. A total of 928 participants provided stool microbiome data, urine for CMV testing, skin prick tests, and questionnaire-based detailed environmental exposures. Cytomegalovirus infection was assessed in the first year of life while the main outcome was defined by persistent sensitization to any allergen at ages 1, 3, and 5 years. RESULTS: Early CMV infection was associated with increased beta and decreased alpha diversity of the gut microbiota. Both changes in diversity measures and early CMV infection were associated with persistent allergic sensitization at age 5 years (aOR = 2.08; 95% CI: 1, 4.33). Mediation analysis demonstrated that perturbation of gut microbial composition explains 30% of the association. CONCLUSIONS: Early-life CMV infection is associated with an alteration in the intestinal microbiota, which mediates the effect of the infection on childhood atopy. This work indicates that preventing CMV infection would not put children at increased risk of developing atopy. Rather, a CMV vaccine, in addition to preventing CMV-associated morbidity and mortality, might reduce the risk of childhood allergic diseases.

A rich meconium metabolome in human infants is associated with early-life gut microbiota composition and reduced allergic sensitization.

Microbiota maturation and immune development occur in parallel with, and are implicated in, allergic diseases, and research has begun to demonstrate the importance of prenatal influencers on both. Here, we investigate the meconium metabolome, a critical link between prenatal exposures and both early microbiota and immune development, to identify components of the neonatal gut niche that contribute to allergic sensitization. Our analysis reveals that newborns who develop immunoglobulin E (IgE)-mediated allergic sensitization (atopy) by 1 year of age have a less-diverse gut metabolome at birth, and specific metabolic clusters are associated with both protection against atopy and the abundance of key taxa driving microbiota maturation. These metabolic signatures, when coupled with early-life microbiota and clinical factors, increase our ability to accurately predict whether or not infants will develop atopy. Thus, the trajectory of both microbiota colonization and immune development are significantly affected by metabolites present in the neonatal gut at birth.

Composition and Associations of the Infant Gut Fungal Microbiota with Environmental Factors and Childhood Allergic Outcomes.

Although often neglected in gut microbiota studies, recent evidence suggests that imbalanced, or dysbiotic, gut mycobiota (fungal microbiota) communities in infancy coassociate with states of bacterial dysbiosis linked to inflammatory diseases such as asthma. In the present study, we (i) characterized the infant gut mycobiota at 3 months and 1 year of age in 343 infants from the CHILD Cohort Study, (ii) defined associations among gut mycobiota community composition and environmental factors for the development of inhalant allergic sensitization (atopy) at age 5 years, and (iii) built a predictive model for inhalant atopy status at age 5 years using these data. We show that in Canadian infants, fungal communities shift dramatically in composition over the first year of life. Early-life environmental factors known to affect gut bacterial communities were also associated with differences in gut fungal community alpha diversity, beta diversity, and/or the relative abundance of specific fungal taxa. Moreover, these metrics differed among healthy infants and those who developed inhalant allergic sensitization (atopy) by age 5 years. Using a rationally selected set of early-life environmental factors in combination with fungal community composition at 1 year of age, we developed a machine learning logistic regression model that predicted inhalant atopy status at 5 years of age with 81% accuracy. Together, these data suggest an important role for the infant gut mycobiota in early-life immune development and indicate that early-life behavioral or therapeutic interventions have the potential to modify infant gut fungal communities, with implications for an infant's long-term health. IMPORTANCE Recent evidence suggests an immunomodulatory role for commensal fungi (mycobiota) in the gut, yet little is known about the composition and dynamics of early-life gut fungal communities. In this work, we show for the first time that the composition of the gut mycobiota of Canadian infants changes dramatically over the course of the first year of life, is associated with environmental factors such as geographical location, diet, and season of birth, and can be used in conjunction with knowledge of a small number of key early-life factors to predict inhalant atopy status at age 5 years. Our study highlights the importance of considering fungal communities as indicators or inciters of immune dysfunction preceding the onset of allergic disease and can serve as a benchmark for future studies aiming to examine infant gut fungal communities across birth cohorts.

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.

Decreasing antibiotic use, the gut microbiota, and asthma incidence in children: evidence from population-based and prospective cohort studies.

BACKGROUND: Childhood asthma incidence is decreasing in some parts of Europe and North America. Antibiotic use in infancy has been associated with increased asthma risk. In the present study, we tested the hypothesis that decreases in asthma incidence are linked to reduced antibiotic prescribing and mediated by changes in the gut bacterial community. METHODS: This study comprised population-based and prospective cohort analyses. At the population level, we used administrative data from British Columbia, Canada (population 4·7 million), on annual rates of antibiotic prescriptions and asthma diagnoses, to assess the association between antibiotic prescribing (at age <1 year) and asthma incidence (at age 1-4 years). At the individual level, 2644 children from the Canadian Healthy Infant Longitudinal Development (CHILD) prospective birth cohort were examined for the association of systemic antibiotic use (at age <1 year) with the diagnosis of asthma (at age 5 years). In the same cohort, we did a mechanistic investigation of 917 children with available 16S rRNA gene sequencing data from faecal samples (at age ≤1 year), to assess how composition of the gut microbiota relates to antibiotic exposure and asthma incidence. FINDINGS: At the population level between 2000 and 2014, asthma incidence in children (aged 1-4 years) showed an absolute decrease of 7·1 new diagnoses per 1000 children, from 27·3 (26·8-28·3) per 1000 children to 20·2 (19·5-20·8) per 1000 children (a relative decrease of 26·0%). Reduction in incidence over the study period was associated with decreasing antibiotic use in infancy (age <1 year), from 1253·8 prescriptions (95% CI 1219·3-1288·9) per 1000 infants to 489·1 (467·6-511·2) per 1000 infants (Spearman's r=0·81; p<0·0001). Asthma incidence increased by 24% with each 10% increase in antibiotic prescribing (adjusted incidence rate ratio 1·24 [95% CI 1·20-1·28]; p<0·0001). In the CHILD cohort, after excluding children who received antibiotics for respiratory symptoms, asthma diagnosis in childhood was associated with infant antibiotic use (adjusted odds ratio [aOR] 2·15 [95% CI 1·37-3·39]; p=0·0009), with a significant dose-response; 114 (5·2%) of 2182 children unexposed to antibiotics had asthma by age 5 years, compared with 23 (8·1%) of 284 exposed to one course, five (10·2%) of 49 exposed to two courses, and six (17·6%) of 34 exposed to three or more courses (aOR 1·44 [1·16-1·79]; p=0·0008). Increasing α-diversity of the gut microbiota, defined as an IQR increase (25th to 75th percentile) in the Chao1 index, at age 1 year was associated with a 32% reduced risk of asthma at age 5 years (aOR for IQR increase 0·68 [0·46-0·99]; p=0·046). In a structural equation model, we found the gut microbiota at age 1 year, characterised by α-diversity, ß-diversity, and amplicon sequence variants modified by antibiotic exposure, to be a significant mediator between outpatient antibiotic exposure in the first year of life and asthma diagnosis at age 5 years (ß=0·08; p=0·027). INTERPRETATION: Our findings suggest that the reduction in the incidence of paediatric asthma observed in recent years might be an unexpected benefit of prudent antibiotic use during infancy, acting via preservation of the gut microbial community. FUNDING: British Columbia Ministry of Health, Pharmaceutical Services Branch; Canadian Institutes of Health Research; Allergy, Genes and Environment (AllerGen) Network of Centres of Excellence; Genome Canada; and Genome British Columbia.

Clinical Utility of a Genomic Classifier in Men Undergoing Radical Prostatectomy: The PRO-IMPACT Trial.

PURPOSE: The optimal management of men with prostate cancer at high risk of recurrence postradical prostatectomy is controversial. The clinical utility of the Decipher test was evaluated prospectively on postoperative treatment decisions and patient-reported outcomes. METHODS AND MATERIALS: In the study, 246 eligible men across 19 centers were enrolled. Patients were dichotomized into those considering adjuvant or salvage radiation therapy (ART or SRT). Participating providers submitted a management recommendation before and after receiving the Decipher test results. Treatment received within 12 months and a validated survey on prostate cancer-related anxiety were collected longitudinally. RESULTS: Pre-Decipher, treatment was recommended for 12% and 40% for the ART and SRT arms, respectively. Post-Decipher, 17% and 30% of treatment recommendations changed in the ART and SRT arms, respectively. Post-Decipher treatment recommendation was administered 78% and 76% of the time in the ART and SRT arms, respectively. Multivariable analysis confirmed that the Decipher score was an independent predictor for change in management for both adjuvant and salvage patients. The number needed to test to change management for one patient was 4. Cancer-specific anxiety decreased among Decipher risk categories in both arms. CONCLUSIONS: Use of Decipher postradical prostatectomy test was associated with postoperative treatment decisions. Overall, high Decipher risk was associated with an increase in treatment intensity whereas low risk scores were associated with a decrease in therapy administered independent of clinical and pathologic risk factors.

Reduced genetic potential for butyrate fermentation in the gut microbiome of infants who develop allergic sensitization.

BACKGROUND: Allergic disease is the most frequent chronic health issue in children and has been linked to early-life gut microbiome dysbiosis. Many lines of evidence suggest that microbially derived short-chain fatty acids, and particularly butyrate, can promote immune tolerance. OBJECTIVE: We sought to determine whether bacterial butyrate production in the gut during early infancy is protective against the development of atopic disease in children. METHODS: We used shotgun metagenomic analysis to determine whether dysbiosis in butyrate fermentation could be identified in human infants, before their developing allergic disease. RESULTS: We found that the microbiome of infants who went on to develop allergic sensitization later in childhood lacked genes encoding key enzymes for carbohydrate breakdown and butyrate production. CONCLUSIONS: Our findings support the importance of microbial carbohydrate metabolism during early infancy in protecting against the development of allergies.

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.

Multiplexed LC-ESI-MRM-MS-based Assay for Identification of Coronary Artery Disease Biomarkers in Human Plasma.

PURPOSE: A highly-multiplexed LC-ESI-multiple reaction monitoring-MS-based assay is developed for the identification of coronary artery disease (CAD) biomarkers in human plasma. EXPERIMENTAL DESIGN: The assay is used to measure 107 stable isotope labeled peptide standards and native peptides from 64 putative biomarkers of cardiovascular diseases in tryptic digests of plasma from subjects with (n = 70) and without (n = 45) angiographic evidence of CAD and no subsequent cardiovascular mortality during follow-up. RESULTS: Extensive computational and statistical analysis reveals six plasma proteins associated with CAD, namely apolipoprotein CII, C reactive protein, CD5 antigen-like, fibronectin, inter alpha trypsin inhibitor heavy chain H1, and protein S. The identified proteins are combined into a LASSO-logistic score with high classification performance (cross-validated area under the curve = 0.74). When combined with a separate score computed from markers currently used in the clinic with similar performance, the area under the receiver operating curve increases to 0.84. Similar results are observed in an independent set of subjects (n = 87). CONCLUSIONS AND CLINICAL RELEVANCE: If externally validated, the assay and identified biomarkers can improve CAD risk stratification.

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.

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.