BackgroundHeart failure (HF) prevalence is increasing in the United States. Mechanical Circulatory Support (MCS) therapy is an option for Advanced HF (AdHF) patients. Perioperatively, multiorgan dysfunction (MOD) is linked to the effects of device implantation, augmented by preexisting HF. Early recognition of MOD allows for better diagnosis, treatment, and risk prediction. Gene expression profiling (GEP) was used to evaluate clinical phenotypes of peripheral blood mononuclear cells (PBMC) transcriptomes obtained from patients' blood samples. Whole blood (WB) samples are clinically more feasible, but their performance in comparison to PBMC samples has not been determined.MethodsWe collected blood samples from 31 HF patients (57±15 years old) undergoing cardiothoracic surgery and 7 healthy age-matched controls, between 2010 and 2011, at a single institution. WB and PBMC samples were collected at a single timepoint postoperatively (median day 8 postoperatively) (25–75% IQR 7–14 days) and subjected to Illumina single color Human BeadChip HT12 v4 whole genome expression array analysis. The Sequential Organ Failure Assessment (SOFA) score was used to characterize the severity of MOD into low (≤ 4 points), intermediate (5–11), and high (≥ 12) risk categories correlating with GEP.ResultsResults indicate that the direction of change in GEP of individuals with MOD as compared to controls is similar when determined from PBMC versus WB. The main enriched terms by Gene Ontology (GO) analysis included those involved in the inflammatory response, apoptosis, and other stress response related pathways. The data revealed 35 significant GO categories and 26 pathways overlapping between PBMC and WB. Additionally, class prediction using machine learning tools demonstrated that the subset of significant genes shared by PBMC and WB are sufficient to train as a predictor separating the SOFA groups.ConclusionGEP analysis of WB has the potential to become a clinical tool for immune-monitoring in patients with MOD.
baseline and 1 yr, and rapid plaque progression (∆MIT 0.5 mm or ∆MIA 3.5 mm 2) were compared. Results: Mean recipient and donor age were 56.1 ± 12.7 and 33.3 ± 13.1 yr old, respectively. 65 (63.1%) received hearts from donors < 40 yr old (Group 1), and 38 (36.9%), from donors ≥ 40 yr old (Group 2). Recipient age was higher in Group 2 (52.9 ± 13.4 vs 61.7 ± 9.0 p< .001). Recipient and donor genders, high risk cytomegalovirus and sensitized status, antithymocyte globulin induction, post-transplant donor specific antibodies, and first yr mTOR inhibitor use were not significantly different between groups (all p> .10). Plaque on baseline IVUS was significantly higher in Group 2 (MIT: 0.36 ± 0.29 vs 0.71 ± 0.41 mm p< .001; MIA: 2.8 ± 1.7 vs 5.2 ± 3.0 mm2 p < .001; PAV: 9.9 ± 7.6 vs 15.9 vs 9.3 % p= .001), sustained at 1 yr (all p< .001). Every 10 yr increase in donor age, plaque on baseline IVUS increased by 0.14 mm MIT, 0.9 mm2 MIA, and 2.4% PAV by linear regression (all p< .001). Progression of plaque between baseline and 1 yr IVUS was not different between groups (MIT: 0.29 ± 0.30 vs 0.29 ± 0.25 mm p= .983; MIA: 1.6 ± 2.1 vs 1.9 ± 2.3 mm2 p= .631; PAV: 5.5 ± 6.4 vs 7.5 ± 7.7 % p= .192) nor was frequency of rapid plaque progression (12/65 vs 8/38 p= .799). Plaque progression was not associated with age by linear regression (all p> .10). Conclusion: Increasing donor age, although associated with modest increases in plaque burden noted on baseline IVUS, is not associated with accelerated plaque progression post-transplant. The implications of these findings for donor allograft selection warrant further investigations.
Background: Endomyocardial Biopsy ( EMB) is the standard method to diagnose allograft rejection post HTx. While it is used to support medical decisions, insufficient diagnostic accuracy constitutes a fundamental limitation. The aim of this study is to develop a methodology that improves the classification of the EMB through a non-supervised evaluation of intramyocardial gene expression. Methods: Sixty-four heart tissues from 47 HTx recipients were subjected to genome wide mRNA sequencing. An unsupervised algorithm using optimal transport to mitigate batch effects and to filter confounding sources of variability was developed to identify molecular signatures of rejection. Linear Mixed Model identified genes statistically significant among the histology defined rejection groups. Weighted Gene Correlation Network Analysis (WGCNA) was used to establish 13 eigengene modules and module-clinical phenotype relationships. Gene Ontology was used for interpretation of the modules in their biological context. Results: O ur algorithm best classified the EMBs into 4 unsupervised clusters solely based on their gene expression. Statistical analysis showed a set of genes differentially expressed among groups defined by histology criteria. Top ranked genes were CLNK, TNFRSF10A, TRADD, CD2, and HLA-A. WGCNA revealed best trait-module correlation was observed between the classes defined by the unsupervised algorithm developed in this study followed by Histology. Figure 1 shows Module-Trait relationships, strength of association, significance and enriched biological process. Conclusion: We have developed an unsupervised algorithm that classifies the EMBs into 4 functionally distinctive categories. These categories are highly correlated with genomic modules defined by WGCNA and with the clinical phenotypes. To our knowledge, this is the first unsupervised classification of the EMBs. Further validation and performance will be provided at the time of presentation.
Background: A key factor underlying health disparity is the lack of interrelatedness. Social Media has become a very popular form of interaction among both minorities and non-minorities. Any single day millions interact through social media reducing the distance between people from diverse cultural backgrounds. We hypothesized that targeted social network intervention strategies effectively reach minorities to promote cardiovascular disease awareness. Method: We used the popular social media platform Twitter to deliver content during regular times or during major events involving people with mixed backgrounds. We first developed a diverse social media community; we then engaged with the audience, identified influencers and delivered different types of content at different moments, linked or unlinked to trending hashtags. We evaluated general social media metrics including impressions, engagement and behavior. Results: There was a significant variation in our ability to reach the audience, cause an impression and trigger a behavior. Trending Hashtag targeting was associated with the highest exposure, however with low engagement rates. Influencer-driven content delivery was linked to highest enrichment and intermediate exposure while non-targeted content delivery was associated with the lowest exposure and engagement rates. Other factors including the time of delivery, quality, type and content were also important determinants of effectiveness. The figure shows an example of a single “tweet” during the May 2, 2015 Mayweather-Pacquiao Box event and the exposed network. Conclusion: Carefully planned Social Network Health interventions are highly effective to reach and engage mixed audiences and influence peers to increase awareness about cardiovascular health. Social media and big data analytics allows the development of novel strategies responsive to the specific needs of culturally diverse audiences.
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