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Introduction: Gut microbial imbalance may contribute to endotoxemia, inflammation and oxidative stress in heart failure (HF). Changes occurring in the oral microbiota and inflammatory/oxidative milieu during HF progression are unknown. Hypothesis: The oral microbiome will be associated with HF severity New York Heart Association, Class I/II, III, IV. Methods: We enrolled 167 patients with HF with reduced ejection fraction (mean age 59±14, 85% Male, 47% White, mean LVEF 20±9, 49% smokers). Biomarkers of endotoxemia (LPS, sCD14) and inflammation (CRP, IL6, TNF-α) were measured in 150 blood samples. A total of 167 oral samples were analyzed using 16S rRNA sequencing. Alpha diversity was assessed via Shannon Index and regressed on HF severity and biomarker levels with linear models. The association between HF severity and Beta diversity (Bray-Curtis dissimilarity) was assessed with PERMANOVA. DESeq2 regressed taxa abundance on HF severity and below vs. above median biomarker levels; multiple comparisons were controlled with the false discovery rate. Models were adjusted for age, sex, race/ethnicity, and smoking status. Results: Mean Shannon Index values±SD across HF class I/II (N=50), III (N=65), and IV (N=52) were 3.28±0.47, 3.42±0.49, 3.20±0.78. NYHA class was associated with both Alpha (pFigure ). Conclusions: HF severity, inflammation, and endotoxemia were associated with oral microbial diversity and bacteria linked to poor oral health, including Treponema denticola and Porphyromonas gingivalis .

Introduction: The HeartMate (HM3) left ventricular assist device (LVAD) uniquely features an artificial pulse (AP) (designed to reduce blood stasis and simulate physiologic pulsatility) that alters arterial blood pressure (BP) tracings ( Fig. 1A ). Pulsatility Index (PI) corresponds to the magnitude of flow pulse through the LVAD and is used as a surrogate measure of arterial pulse pressure (PP). The effect of the AP on: i) relative contribution of systolic BP (SBP) and diastolic BP (DBP) to mean arterial pressure (MAP) calculation; and ii) association between PP and PI is presently unknown. Thus, we aimed to compare: i) MAP calculations; and ii) relation of PI with PP in HM3 vs HM II (LVAD with no AP) pts with arterial line (A-line) monitoring. Methods: A-line BP and LVAD PI data were prospectively collected in 48 HM3 and 29 HMII pts. MAP was calculated with the formula conventionally used in non LVAD pts (MAP = 2/3 DBP + 1/3 SBP) and compared to A-line MAP. Among HM3 pts, a multiple linear regression model was fit with A-line SBP and DBP as predictor variables, and A-line MAP as the dependent variable to derive the HM3 MAP Formula . The relation between arterial PP and PI in HM3 and HMII pts was assessed using Pearson’s correlation. Results: MAP calculated using the conventional formula accurately estimated A-line MAP in HMII pts, but overestimated A-line MAP in HM3 pts. The HM3 MAP Formula more closely approximated A-line MAP. Mean observed difference (MOD) and mean absolute difference (MAD) between calculated MAPs and A-line MAPs are reported in Fig. 1B . While median PP was similar in HM3 and HMII pts (16 vs 20 mmHg, p=0.11), median PI was significantly higher in HMII pts (3.45 vs 5.6, pr 0.47, p=0.01). However, no significant correlation was found between PI and PP in HM3 pts ( r 0.24, p=0.1; Fig. 1C ). Conclusions: In HM3 pts, the AP significantly alters the relative contribution of SBP and DBP to MAP. Unlike in HM2 pts, PI does not relate to arterial PP in HM3 pts.

Introduction: Cardiac allograft vasculopathy (CAV) is a leading cause of death after heart transplant (HT). Inflammation is a known cardiovascular risk factor, with gut derived endotoxemia potentially instigating this process, leading to endothelial damage. Our prior work showed that elevation of biomarkers of inflammation (endothelin-1 (ET-1)) and endotoxemia (lipopolysaccharide (LPS)) persists after HT. We investigated the association of LPS and ET-1 with subsequent development of CAV. Methods: Pts who met the following criteria were included: i) blood sampling ≥6 mo post-HT; ii) no evidence of CAV at the time of sampling. Pts were followed up to 2.3y after sampling. Cox-PH was used to regress freedom from CAV on LPS and ET-1 (≤ vs >median), time from HT to sampling (≤ vs >1y), interaction among variables. Results: 33 HT pts enrolled, mean age 57±11y; 70% male; median time post-HT 1.96(0.5-3.7)y. 17(52%) pts developed CAV at 0.7(0.6-1)y following blood collection. Baseline characteristics of pts who developed CAV vs not were similar, except for longer median time post-HT in pts who developed CAV: 2.4(0.7-5.3) vs 1.05(0.5-2.7)y, p=0.1. Median LPS and ET-1 for the entire cohort were 0.34(0.28-0.45) EU/ml and 1.83(1.26-2.72) pg/ml. Pts with >median LPS or ET-1 values trended towards higher risk of developing CAV: HR 1.79(0.66, 4.85), p=0.3 ( Fig.1A ); HR 2.05(0.71, 5.92), p=0.2 ( Fig.1B ). No association was observed between time post-HT and CAV: HR=0.87 comparing ≤ vs. >1y, p=0.8. Pts with both >median LPS and ET-1 levels (n=7) were at significantly higher risk of CAV: HR 2.96(1.06, 8.27), p=0.039, compared to others ( Fig.1C ). When examining pts sampled within the first year of HT (N=14), pts with >median LPS (N=5) were at particularly increased risk of CAV: HR 9.71(0.97, 96.9), p=0.05, while pts with >median ET-1 were not. Conclusions: Elevated LPS and ET-1 synergistically associate with increased CAV risk post-HT. Further studies are warranted to validate these findings.