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Background Experimental studies support a link between obesity and pulmonary hypertension (PH), yet clinical studies have been limited. This study sought to determine the association of obesity and pulmonary hemodynamic measures and mortality in PH. Methods and Results We examined patients undergoing right‐sided heart catherization (2005–2016) in a hospital‐based cohort. Multivariable regression models tested associations of body mass index and pulmonary vascular hemodynamics, with PH defined as mean pulmonary artery pressure >20 mm Hg, and further subclassified into precapillary, postcapillary, and mixed PH. Multivariable Cox models were used to examine the effect of PH and obesity on mortality. Among 8940 patients (mean age, 62 years; 40% women), 52% of nonobese and 69% of obese individuals had evidence of PH. Higher body mass index was independently associated with greater odds of overall PH (odds ratio, 1.34; 95% CI, 1.29–1.40; P <0.001 per 5‐unit increase in body mass index) as well as each PH subtype ( P <0.001 for all). Patients with PH had greater risk of mortality compared with individuals without PH regardless of subgroup ( P <0.001 for all). We found that obesity was associated with 23% lower hazard of mortality among patients with PH (hazard ratio, 0.77; 95% CI, 0.69–0.85; P <0.001). The effect of obesity was greatest among those with precapillary PH (hazard ratio, 0.57; 95% CI, 0.46–0.70; P <0.001), where obesity modified the effect of PH on mortality ( P for interaction=0.02). Conclusions Obesity is independently associated with PH. PH is associated with greater mortality; this is modified by obesity such that obese patients with precapillary PH have lower mortality compared with nonobese counterparts. Further studies are needed to elucidate mechanisms underlying obesity‐related PH.

We carried out integrated host and pathogen metagenomic RNA and DNA next generation sequencing (mNGS) of whole blood (n = 221) and plasma (n = 138) from critically ill patients following hospital admission. We assigned patients into sepsis groups on the basis of clinical and microbiological criteria. From whole-blood gene expression data, we distinguished patients with sepsis from patients with non-infectious systemic inflammatory conditions using a trained bagged support vector machine (bSVM) classifier (area under the receiver operating characteristic curve (AUC) = 0.81 in the training set; AUC = 0.82 in a held-out validation set). Plasma RNA also yielded a transcriptional signature of sepsis with several genes previously reported as sepsis biomarkers, and a bSVM sepsis diagnostic classifier (AUC = 0.97 training set; AUC = 0.77 validation set). Pathogen detection performance of plasma mNGS varied on the basis of pathogen and site of infection. To improve detection of virus, we developed a secondary transcriptomic classifier (AUC = 0.94 training set; AUC = 0.96 validation set). We combined host and microbial features to develop an integrated sepsis diagnostic model that identified 99% of microbiologically confirmed sepsis cases, and predicted sepsis in 74% of suspected and 89% of indeterminate sepsis cases. In summary, we suggest that integrating host transcriptional profiling and broad-range metagenomic pathogen detection from nucleic acid is a promising tool for sepsis diagnosis.