Mendelian Randomization Study With Clinical Follow-Up Links Metabolites to Risk and Severity of Pulmonary Arterial Hypertension.

BACKGROUND: Pulmonary arterial hypertension (PAH) exhibits phenotypic heterogeneity and variable response to therapy. The metabolome has been implicated in the pathogenesis of PAH, but previous works have lacked power to implicate specific metabolites. Mendelian randomization (MR) is a method for causal inference between exposures and outcomes. METHODS AND RESULTS: Using genome-wide association study summary statistics, we implemented MR analysis to test for potential causal relationships between serum concentration of 575 metabolites and PAH. Five metabolites were causally associated with the risk of PAH after multiple testing correction. Next, we measured serum concentration of candidate metabolites in an independent clinical cohort of 449 patients with PAH to check whether metabolite concentrations are correlated with markers of disease severity. Of the 5 candidates nominated by our MR work, serine was negatively associated and homostachydrine was positively associated with clinical severity of PAH via direct measurement in this independent clinical cohort. Finally we used conditional and orthogonal approaches to explore the biology underlying our lead metabolites. Rare variant burden testing was carried out using whole exome sequencing data from 578 PAH cases and 361 675 controls. Multivariable MR is an extension of MR that uses a single set of instrumental single-nucleotide polymorphisms to measure multiple exposures; multivariable MR is used to determine interdependence between the effects of different exposures on a single outcome. Rare variant analysis demonstrated that loss-of-function mutations within activating transcription factor 4, a transcription factor responsible for upregulation of serine synthesis under conditions of serine starvation, are associated with higher risk for PAH. Homostachydrine is a xenobiotic metabolite that is structurally related to l-proline betaine, which has previously been linked to modulation of inflammation and tissue remodeling in PAH. Our multivariable MR analysis suggests that the effect of l-proline betaine is actually mediated indirectly via homostachydrine. CONCLUSIONS: Our data present a method for study of the metabolome in the context of PAH, and suggests several candidates for further evaluation and translational research.

Unsupervised machine learning to investigate trajectory patterns of COVID-19 symptoms and physical activity measured via the MyHeart Counts App and smart devices.

Previous studies have associated COVID-19 symptoms severity with levels of physical activity. We therefore investigated longitudinal trajectories of COVID-19 symptoms in a cohort of healthcare workers (HCWs) with non-hospitalised COVID-19 and their real-world physical activity. 121 HCWs with a history of COVID-19 infection who had symptoms monitored through at least two research clinic visits, and via smartphone were examined. HCWs with a compatible smartphone were provided with an Apple Watch Series 4 and were asked to install the MyHeart Counts Study App to collect COVID-19 symptom data and multiple physical activity parameters. Unsupervised classification analysis of symptoms identified two trajectory patterns of long and short symptom duration. The prevalence for longitudinal persistence of any COVID-19 symptom was 36% with fatigue and loss of smell being the two most prevalent individual symptom trajectories (24.8% and 21.5%, respectively). 8 physical activity features obtained via the MyHeart Counts App identified two groups of trajectories for high and low activity. Of these 8 parameters only 'distance moved walking or running' was associated with COVID-19 symptom trajectories. We report a high prevalence of long-term symptoms of COVID-19 in a non-hospitalised cohort of HCWs, a method to identify physical activity trends, and investigate their association. These data highlight the importance of tracking symptoms from onset to recovery even in non-hospitalised COVID-19 individuals. The increasing ease in collecting real-world physical activity data non-invasively from wearable devices provides opportunity to investigate the association of physical activity to symptoms of COVID-19 and other cardio-respiratory diseases.

Biological heterogeneity in idiopathic pulmonary arterial hypertension identified through unsupervised transcriptomic profiling of whole blood.

Idiopathic pulmonary arterial hypertension (IPAH) is a rare but fatal disease diagnosed by right heart catheterisation and the exclusion of other forms of pulmonary arterial hypertension, producing a heterogeneous population with varied treatment response. Here we show unsupervised machine learning identification of three major patient subgroups that account for 92% of the cohort, each with unique whole blood transcriptomic and clinical feature signatures. These subgroups are associated with poor, moderate, and good prognosis. The poor prognosis subgroup is associated with upregulation of the ALAS2 and downregulation of several immunoglobulin genes, while the good prognosis subgroup is defined by upregulation of the bone morphogenetic protein signalling regulator NOG, and the C/C variant of HLA-DPA1/DPB1 (independently associated with survival). These findings independently validated provide evidence for the existence of 3 major subgroups (endophenotypes) within the IPAH classification, could improve risk stratification and provide molecular insights into the pathogenesis of IPAH.

A diagnostic miRNA signature for pulmonary arterial hypertension using a consensus machine learning approach.

BACKGROUND: Pulmonary arterial hypertension (PAH) is a rare but life shortening disease, the diagnosis of which is often delayed, and requires an invasive right heart catheterisation. Identifying diagnostic biomarkers may improve screening to identify patients at risk of PAH earlier and provide new insights into disease pathogenesis. MicroRNAs are small, non-coding molecules of RNA, previously shown to be dysregulated in PAH, and contribute to the disease process in animal models. METHODS: Plasma from 64 treatment naïve patients with PAH and 43 disease and healthy controls were profiled for microRNA expression by Agilent Microarray. Following quality control and normalisation, the cohort was split into training and validation sets. Four separate machine learning feature selection methods were applied to the training set, along with a univariate analysis. FINDINGS: 20 microRNAs were identified as putative biomarkers by consensus feature selection from all four methods. Two microRNAs (miR-636 and miR-187-5p) were selected by all methods and used to predict PAH diagnosis with high accuracy. Integrating microRNA expression profiles with their associated target mRNA revealed 61 differentially expressed genes verified in two independent, publicly available PAH lung tissue data sets. Two of seven potentially novel gene targets were validated as differentially expressed in vitro in human pulmonary artery smooth muscle cells. INTERPRETATION: This consensus of multiple machine learning approaches identified two miRNAs that were able to distinguish PAH from both disease and healthy controls. These circulating miRNA, and their target genes may provide insight into PAH pathogenesis and reveal novel regulators of disease and putative drug targets. FUNDING: This work was supported by a National Institute for Health Research Rare Disease Translational Research Collaboration (R29065/CN500) and British Heart Foundation Project Grant (PG/11/116/29288).

Whole-Blood RNA Profiles Associated with Pulmonary Arterial Hypertension and Clinical Outcome.

Rationale: Idiopathic and heritable pulmonary arterial hypertension (PAH) are rare but comprise a genetically heterogeneous patient group. RNA sequencing linked to the underlying genetic architecture can be used to better understand the underlying pathology by identifying key signaling pathways and stratify patients more robustly according to clinical risk.Objectives: To use a three-stage design of RNA discovery, RNA validation and model construction, and model validation to define a set of PAH-associated RNAs and a single summarizing RNA model score. To define genes most likely to be involved in disease development, we performed Mendelian randomization (MR) analysis.Methods: RNA sequencing was performed on whole-blood samples from 359 patients with idiopathic, heritable, and drug-induced PAH and 72 age- and sex-matched healthy volunteers. The score was evaluated against disease severity markers including survival analysis using all-cause mortality from diagnosis. MR used known expression quantitative trait loci and summary statistics from a PAH genome-wide association study.Measurements and Main Results: We identified 507 genes with differential RNA expression in patients with PAH compared with control subjects. A model of 25 RNAs distinguished PAH with 87% accuracy (area under the curve 95% confidence interval: 0.791-0.945) in model validation. The RNA model score was associated with disease severity and long-term survival (P = 4.66 × 10-6) in PAH. MR detected an association between SMAD5 levels and PAH disease susceptibility (odds ratio, 0.317; 95% confidence interval, 0.129-0.776; P = 0.012).Conclusions: A whole-blood RNA signature of PAH, which includes RNAs relevant to disease pathogenesis, associates with disease severity and identifies patients with poor clinical outcomes. Genetic variants associated with lower SMAD5 expression may increase susceptibility to PAH.