Investigating the impact of socioeconomic status on amyotrophic lateral sclerosis.

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease characterized by the gradual death of motor neurons in the brain and spinal cord, leading to fatal paralysis. Socioeconomic status (SES) is a measure of an individual's shared economic and social status, which has been shown to have an association with health outcomes. Understanding the impact of SES on health conditions is crucial, as it can influence and be influenced by health-related variables. The role of socioeconomic status in influencing the risk and progression of ALS has not been established, and understanding the various factors that impact ALS is important in developing strategies for treatment and prevention. To investigate this relationship, we recruited 413 participants with definite, probable, or possible ALS according to the El Escorial criteria, from three tertiary centers in London, Sheffield, and Birmingham. Logistic regression was used to examine the association between case-control status, socioeconomic criteria, and ALS risk. Linear regression was used to examine the association between age of onset and socioeconomic variables. Two sensitivity analyses were performed, one using an alternative occupational classifier, and the other using Mendelian Randomization analysis to examine association. There was no significant relationship between any variables and ALS risk. We found an inverse relationship between mean lifetime salary and age of ALS onset (Beta = -0.157, p = 0.011), but no effect of education or occupation on the age of onset. The finding was confirmed in both sensitivity analyses and in Mendelian Randomization. We find that a higher salary is associated with a younger age of ALS onset taking into account sex, occupation, years of education, and clinical presentation.

Dysregulated immunologic landscape of the early host response in melioidosis.

Melioidosis, a neglected tropical infection caused by Burkholderia pseudomallei, commonly presents as pneumonia or sepsis with mortality rates up to 50% despite appropriate treatment. A better understanding of the early host immune response to melioidosis may lead to new therapeutic interventions and prognostication strategies to reduce disease burden. Whole blood transcriptomic signatures in 164 patients with melioidosis and in 70 patients with other infections hospitalized in northeastern Thailand enrolled within 24 hours following hospital admission were studied. Key findings were validated in an independent melioidosis cohort. Melioidosis was characterized by upregulation of interferon (IFN) signaling responses compared with other infections. Mortality in melioidosis was associated with excessive inflammation, enrichment of type 2 immune responses, and a dramatic decrease in T cell-mediated immunity compared with survivors. We identified and independently confirmed a 5-gene predictive set classifying fatal melioidosis (validation cohort area under the receiver operating characteristic curve 0.83; 95% CI, 0.67-0.99). This study highlights the intricate balance between innate and adaptive immunity during fatal melioidosis and can inform future precision medicine strategies for targeted therapies and prognostication in this severe infection.

Iterative Data-adaptive Autoregressive (IDAR) whitening procedure for long and short TR fMRI.

Introduction: Functional magnetic resonance imaging (fMRI) has become a fundamental tool for studying brain function. However, the presence of serial correlations in fMRI data complicates data analysis, violates the statistical assumptions of analyses methods, and can lead to incorrect conclusions in fMRI studies. Methods: In this paper, we show that conventional whitening procedures designed for data with longer repetition times (TRs) (>2 s) are inadequate for the increasing use of short-TR fMRI data. Furthermore, we comprehensively investigate the shortcomings of existing whitening methods and introduce an iterative whitening approach named "IDAR" (Iterative Data-adaptive Autoregressive model) to address these shortcomings. IDAR employs high-order autoregressive (AR) models with flexible and data-driven orders, offering the capability to model complex serial correlation structures in both short-TR and long-TR fMRI datasets. Results: Conventional whitening methods, such as AR(1), ARMA(1,1), and higher-order AR, were effective in reducing serial correlation in long-TR data but were largely ineffective in even reducing serial correlation in short-TR data. In contrast, IDAR significantly outperformed conventional methods in addressing serial correlation, power, and Type-I error for both long-TR and especially short-TR data. However, IDAR could not simultaneously address residual correlations and inflated Type-I error effectively. Discussion: This study highlights the urgent need to address the problem of serial correlation in short-TR (< 1 s) fMRI data, which are increasingly used in the field. Although IDAR can address this issue for a wide range of applications and datasets, the complexity of short-TR data necessitates continued exploration and innovative approaches. These efforts are essential to simultaneously reduce serial correlations and control Type-I error rates without compromising analytical power.

Predicting Out-of-Hospital Cardiac Arrest in the General Population Using Electronic Health Records.

BACKGROUND: The majority of out-of-hospital cardiac arrests (OHCAs) occur among individuals in the general population, for whom there is no established strategy to identify risk. In this study, we assess the use of electronic health record (EHR) data to identify OHCA in the general population and define salient factors contributing to OHCA risk. METHODS: The analytical cohort included 2366 individuals with OHCA and 23 660 age- and sex-matched controls receiving health care at the University of Washington. Comorbidities, electrocardiographic measures, vital signs, and medication prescription were abstracted from the EHR. The primary outcome was OHCA. Secondary outcomes included shockable and nonshockable OHCA. Model performance including area under the receiver operating characteristic curve and positive predictive value were assessed and adjusted for observed rate of OHCA across the health system. RESULTS: There were significant differences in demographic characteristics, vital signs, electrocardiographic measures, comorbidities, and medication distribution between individuals with OHCA and controls. In external validation, discrimination in machine learning models (area under the receiver operating characteristic curve 0.80-0.85) was superior to a baseline model with conventional cardiovascular risk factors (area under the receiver operating characteristic curve 0.66). At a specificity threshold of 99%, correcting for baseline OHCA incidence across the health system, positive predictive value was 2.5% to 3.1% in machine learning models compared with 0.8% for the baseline model. Longer corrected QT interval, substance abuse disorder, fluid and electrolyte disorder, alcohol abuse, and higher heart rate were identified as salient predictors of OHCA risk across all machine learning models. Established cardiovascular risk factors retained predictive importance for shockable OHCA, but demographic characteristics (minority race, single marital status) and noncardiovascular comorbidities (substance abuse disorder) also contributed to risk prediction. For nonshockable OHCA, a range of salient predictors, including comorbidities, habits, vital signs, demographic characteristics, and electrocardiographic measures, were identified. CONCLUSIONS: In a population-based case-control study, machine learning models incorporating readily available EHR data showed reasonable discrimination and risk enrichment for OHCA in the general population. Salient factors associated with OCHA risk were myriad across the cardiovascular and noncardiovascular spectrum. Public health and tailored strategies for OHCA prediction and prevention will require incorporation of this complexity.

Unraveling Alzheimer's Disease: Investigating Dynamic Functional Connectivity in the Default Mode Network through DCC-GARCH Modeling.

Alzheimer's disease (AD) has a prolonged latent phase. Sensitive biomarkers of amyloid beta ( A ß ), in the absence of clinical symptoms, offer opportunities for early detection and identification of patients at risk. Current A ß biomarkers, such as CSF and PET biomarkers, are effective but face practical limitations due to high cost and limited availability. Recent blood plasma biomarkers, though accessible, still incur high costs and lack physiological significance in the Alzheimer's process. This study explores the potential of brain functional connectivity (FC) alterations associated with AD pathology as a non-invasive avenue for A ß detection. While current stationary FC measurements lack sensitivity at the single-subject level, our investigation focuses on dynamic FC using resting-state functional MRI (rs-fMRI) and introduces the Generalized Auto-Regressive Conditional Heteroscedastic Dynamic Conditional Correlation (DCC-GARCH) model. Our findings demonstrate the superior sensitivity of DCC-GARCH to CSF A ß status, and offer key insights into dynamic functional connectivity analysis in AD.

Determinants of mosaic chromosomal alteration fitness.

Clonal hematopoiesis (CH) is characterized by the acquisition of a somatic mutation in a hematopoietic stem cell that results in a clonal expansion. These driver mutations can be single nucleotide variants in cancer driver genes or larger structural rearrangements called mosaic chromosomal alterations (mCAs). The factors that influence the variations in mCA fitness and ultimately result in different clonal expansion rates are not well understood. We used the Passenger-Approximated Clonal Expansion Rate (PACER) method to estimate clonal expansion rate as PACER scores for 6,381 individuals in the NHLBI TOPMed cohort with gain, loss, and copy-neutral loss of heterozygosity mCAs. Our mCA fitness estimates, derived by aggregating per-individual PACER scores, were correlated (R2 = 0.49) with an alternative approach that estimated fitness of mCAs in the UK Biobank using population-level distributions of clonal fraction. Among individuals with JAK2 V617F clonal hematopoiesis of indeterminate potential or mCAs affecting the JAK2 gene on chromosome 9, PACER score was strongly correlated with erythrocyte count. In a cross-sectional analysis, genome-wide association study of estimates of mCA expansion rate identified a TCL1A locus variant associated with mCA clonal expansion rate, with suggestive variants in NRIP1 and TERT.

GAD65Abs Are Not Associated With Beta-Cell Dysfunction in Patients With T2D in the GRADE Study.

Context: Autoantibodies directed against the 65-kilodalton isoform of glutamic acid decarboxylase (GAD65Abs) are markers of autoimmune type 1 diabetes (T1D) but are also present in patients with Latent Autoimmune Diabetes of Adults and autoimmune neuromuscular diseases, and also in healthy individuals. Phenotypic differences between these conditions are reflected in epitope-specific GAD65Abs and anti-idiotypic antibodies (anti-Id) against GAD65Abs. We previously reported that 7.8% of T2D patients in the GRADE study have GAD65Abs but found that GAD65Ab positivity was not correlated with beta-cell function, glycated hemoglobin (HbA1c), or fasting glucose levels. Context: In this study, we aimed to better characterize islet autoantibodies in this T2D cohort. This is an ancillary study to NCT01794143. Methods: We stringently defined GAD65Ab positivity with a competition assay, analyzed GAD65Ab-specific epitopes, and measured GAD65Ab-specific anti-Id in serum. Results: Competition assays confirmed that 5.9% of the patients were GAD65Ab positive, but beta-cell function was not associated with GAD65Ab positivity, GAD65Ab epitope specificity or GAD65Ab-specific anti-Id. GAD65-related autoantibody responses in GRADE T2D patients resemble profiles in healthy individuals (low GAD65Ab titers, presence of a single autoantibody, lack of a distinct epitope pattern, and presence of anti-Id to diabetes-associated GAD65Ab). In this T2D cohort, GAD65Ab positivity is likely unrelated to the pathogenesis of beta-cell dysfunction. Conclusion: Evidence for islet autoimmunity in the pathophysiology of T2D beta-cell dysfunction is growing, but T1D-associated autoantibodies may not accurately reflect the nature of their autoimmune process.

Statistical inference on qualitative differences in the magnitude of an effect.

Qualitative interactions occur when a treatment effect or measure of association varies in sign by sub-population. Of particular interest in many biomedical settings are absence/presence qualitative interactions, which occur when an effect is present in one sub-population but absent in another. Absence/presence interactions arise in emerging applications in precision medicine, where the objective is to identify a set of predictive biomarkers that have prognostic value for clinical outcomes in some sub-population but not others. They also arise naturally in gene regulatory network inference, where the goal is to identify differences in networks corresponding to diseased and healthy individuals, or to different subtypes of disease; such differences lead to identification of network-based biomarkers for diseases. In this paper, we argue that while the absence/presence hypothesis is important, developing a statistical test for this hypothesis is an intractable problem. To overcome this challenge, we approximate the problem in a novel inference framework. In particular, we propose to make inferences about absence/presence interactions by quantifying the relative difference in effect size, reasoning that when the relative difference is large, an absence/presence interaction occurs. The proposed methodology is illustrated through a simulation study as well as an analysis of breast cancer data from the Cancer Genome Atlas.

Plasma Metabolomics Reveals Distinct Biological and Diagnostic Signatures for Melioidosis.

Rationale: The global burden of sepsis is greatest in low-resource settings. Melioidosis, infection with the gram-negative bacterium Burkholderia pseudomallei, is a frequent cause of fatal sepsis in endemic tropical regions such as Southeast Asia. Objectives: To investigate whether plasma metabolomics would identify biological pathways specific to melioidosis and yield clinically meaningful biomarkers. Methods: Using a comprehensive approach, differential enrichment of plasma metabolites and pathways was systematically evaluated in individuals selected from a prospective cohort of patients hospitalized in rural Thailand with infection. Statistical and bioinformatics methods were used to distinguish metabolomic features and processes specific to patients with melioidosis and between fatal and nonfatal cases. Measurements and Main Results: Metabolomic profiling and pathway enrichment analysis of plasma samples from patients with melioidosis (n = 175) and nonmelioidosis infections (n = 75) revealed a distinct immuno-metabolic state among patients with melioidosis, as suggested by excessive tryptophan catabolism in the kynurenine pathway and significantly increased levels of sphingomyelins and ceramide species. We derived a 12-metabolite classifier to distinguish melioidosis from other infections, yielding an area under the receiver operating characteristic curve of 0.87 in a second validation set of patients. Melioidosis nonsurvivors (n = 94) had a significantly disturbed metabolome compared with survivors (n = 81), with increased leucine, isoleucine, and valine metabolism, and elevated circulating free fatty acids and acylcarnitines. A limited eight-metabolite panel showed promise as an early prognosticator of mortality in melioidosis. Conclusions: Melioidosis induces a distinct metabolomic state that can be examined to distinguish underlying pathophysiological mechanisms associated with death. A 12-metabolite signature accurately differentiates melioidosis from other infections and may have diagnostic applications.

Analysis of non-motor symptoms in amyotrophic lateral sclerosis.

OBJECTIVE: We investigated non-motor symptoms in ALS using sequential questionnaires; here we report the findings of the second questionnaire. METHODS: A social media platform (Twitter, now known as X) was used to publicize the questionnaires. Data were downloaded from SurveyMonkey and analyzed by descriptive statistics, comparison of means, and regression models. RESULTS: There were 182 people with ALS and 57 controls. The most important non-motor symptoms were cold limbs (60.4% cases, 14% controls, p = 9.67 x 10-10) and appetite loss (29.7% cases, 5.3% controls, p = 1.6 x 10-4). The weaker limb was most likely to feel cold (p = 9.67 x 10-10), and symptoms were more apparent in the evening and night. Appetite loss was reported as due to feeling full and the time taken to eat. People with ALS experienced medium-intensity pain, more usually shock-like pain than burning or cold-like pain, although the most prevalent type of pain was non-differentiated. CONCLUSIONS: Non-motor symptoms are an important feature of ALS. Further investigation is needed to understand their physiological basis and whether they represent phenotypic differences useful for subtyping ALS.

Non-motor symptoms in amyotrophic lateral sclerosis.

OBJECTIVE: While motor symptoms are well-known in ALS, non-motor symptoms are often under-reported and may have a significant impact on quality of life. In this study, we aimed to examine the nature and extent of non-motor symptoms in ALS. METHODS: A 20-item questionnaire was developed covering the domains of autonomic function, sleep, pain, gastrointestinal disturbance, and emotional lability, posted online and shared on social media platforms to target people with ALS and controls. RESULTS: A total of 1018 responses were received, of which 927 were complete from 506 people with ALS and 421 unaffected individuals. Cold limbs (p 1.66 × 10-36), painful limbs (p 6.14 × 10-28), and urinary urgency (p 4.70 × 10-23) were associated with ALS. People with ALS were more likely to report autonomic symptoms, pain, and psychiatric symptoms than controls (autonomic symptoms B = 0.043, p 6.10 × 10-5, pain domain B = 0.18, p 3.72 × 10-11 and psychiatric domain B = 0.173, p 1.32 × 10-4). CONCLUSIONS: Non-motor symptoms in ALS are common. The identification and management of non-motor symptoms should be integrated into routine clinical care for people with ALS. Further research is warranted to investigate the relationship between non-motor symptoms and disease progression, as well as to develop targeted interventions to improve the quality of life for people with ALS.

GENERALIZED MATRIX DECOMPOSITION REGRESSION: ESTIMATION AND INFERENCE FOR TWO-WAY STRUCTURED DATA.

Motivated by emerging applications in ecology, microbiology, and neuroscience, this paper studies high-dimensional regression with two-way structured data. To estimate the high-dimensional coefficient vector, we propose the generalized matrix decomposition regression (GMDR) to efficiently leverage auxiliary information on row and column structures. GMDR extends the principal component regression (PCR) to two-way structured data, but unlike PCR, GMDR selects the components that are most predictive of the outcome, leading to more accurate prediction. For inference on regression coefficients of individual variables, we propose the generalized matrix decomposition inference (GMDI), a general high-dimensional inferential framework for a large family of estimators that include the proposed GMDR estimator. GMDI provides more flexibility for incorporating relevant auxiliary row and column structures. As a result, GMDI does not require the true regression coefficients to be sparse, but constrains the coordinate system representing the regression coefficients according to the column structure. GMDI also allows dependent and heteroscedastic observations. We study the theoretical properties of GMDI in terms of both the type-I error rate and power and demonstrate the effectiveness of GMDR and GMDI in simulation studies and an application to human microbiome data.

Identification of circulating proteins associated with general cognitive function among middle-aged and older adults.

Identifying circulating proteins associated with cognitive function may point to biomarkers and molecular process of cognitive impairment. Few studies have investigated the association between circulating proteins and cognitive function. We identify 246 protein measures quantified by the SomaScan assay as associated with cognitive function (p < 4.9E-5, n up to 7289). Of these, 45 were replicated using SomaScan data, and three were replicated using Olink data at Bonferroni-corrected significance. Enrichment analysis linked the proteins associated with general cognitive function to cell signaling pathways and synapse architecture. Mendelian randomization analysis implicated higher levels of NECTIN2, a protein mediating viral entry into neuronal cells, with higher Alzheimer's disease (AD) risk (p = 2.5E-26). Levels of 14 other protein measures were implicated as consequences of AD susceptibility (p < 2.0E-4). Proteins implicated as causes or consequences of AD susceptibility may provide new insight into the potential relationship between immunity and AD susceptibility as well as potential therapeutic targets.

The functional impact of rare variation across the regulatory cascade.

Each human genome has tens of thousands of rare genetic variants; however, identifying impactful rare variants remains a major challenge. We demonstrate how use of personal multi-omics can enable identification of impactful rare variants by using the Multi-Ethnic Study of Atherosclerosis, which included several hundred individuals, with whole-genome sequencing, transcriptomes, methylomes, and proteomes collected across two time points, 10 years apart. We evaluated each multi-omics phenotype's ability to separately and jointly inform functional rare variation. By combining expression and protein data, we observed rare stop variants 62 times and rare frameshift variants 216 times as frequently as controls, compared to 13-27 times as frequently for expression or protein effects alone. We extended a Bayesian hierarchical model, "Watershed," to prioritize specific rare variants underlying multi-omics signals across the regulatory cascade. With this approach, we identified rare variants that exhibited large effect sizes on multiple complex traits including height, schizophrenia, and Alzheimer's disease.

Determinants of mosaic chromosomal alteration fitness.

Clonal hematopoiesis (CH) is characterized by the acquisition of a somatic mutation in a hematopoietic stem cell that results in a clonal expansion. These driver mutations can be single nucleotide variants in cancer driver genes or larger structural rearrangements called mosaic chromosomal alterations (mCAs). The factors that influence the variations in mCA fitness and ultimately result in different clonal expansion rates are not well-understood. We used the Passenger-Approximated Clonal Expansion Rate (PACER) method to estimate clonal expansion rate for 6,381 individuals in the NHLBI TOPMed cohort with gain, loss, and copy-neutral loss of heterozygosity mCAs. Our estimates of mCA fitness were correlated (R 2 = 0.49) with an alternative approach that estimated fitness of mCAs in the UK Biobank using a theoretical probability distribution. Individuals with lymphoid-associated mCAs had a significantly higher white blood cell count and faster clonal expansion rate. In a cross-sectional analysis, genome-wide association study of estimates of mCA expansion rate identified TCL1A , NRIP1 , and TERT locus variants as modulators of mCA clonal expansion rate.

Non-motor symptoms in amyotrophic lateral sclerosis: lessons from Parkinson's disease.

Amyotrophic lateral sclerosis and Parkinson's disease are neurodegenerative diseases of the motor system which are now recognized also to affect non-motor pathways. Non-motor symptoms have been acknowledged as important determinants of quality of life in Parkinson's disease, and there is increasing interest in understanding the extent and role of non-motor symptoms in amyotrophic lateral sclerosis. We therefore reviewed what is known about non-motor symptoms in amyotrophic lateral sclerosis, using lessons from Parkinson's disease.

Human microglia show unique transcriptional changes in Alzheimer's disease.

Microglia, the innate immune cells of the brain, influence Alzheimer's disease (AD) progression and are potential therapeutic targets. However, microglia exhibit diverse functions, the regulation of which is not fully understood, complicating therapeutics development. To better define the transcriptomic phenotypes and gene regulatory networks associated with AD, we enriched for microglia nuclei from 12 AD and 10 control human dorsolateral prefrontal cortices (7 males and 15 females, all aged >60 years) before single-nucleus RNA sequencing. Here we describe both established and previously unrecognized microglial molecular phenotypes, the inferred gene networks driving observed transcriptomic change, and apply trajectory analysis to reveal the putative relationships between microglial phenotypes. We identify microglial phenotypes more prevalent in AD cases compared with controls. Further, we describe the heterogeneity in microglia subclusters expressing homeostatic markers. Our study demonstrates that deep profiling of microglia in human AD brain can provide insight into microglial transcriptional changes associated with AD.

Plasma Proteomic Associations With Incident Ischemic Stroke in Older Adults: The Cardiovascular Health Study.

BACKGROUND AND OBJECTIVES: Plasma proteomics may elucidate novel insights into the pathophysiology of ischemic stroke (IS), identify biomarkers of IS risk, and guide development of nascent prevention strategies. We evaluated the relationship between the plasma proteome and IS risk in the population-based Cardiovascular Health Study (CHS). METHODS: Eligible CHS participants were free of prevalent stroke and underwent quantification of 1,298 plasma proteins using the aptamer-based SOMAScan assay platform from the 1992-1993 study visit. Multivariable Cox proportional hazards regression was used to evaluate associations between a 1-SD increase in the log2-transformed estimated plasma protein concentrations and incident IS, adjusting for demographics, IS risk factors, and estimated glomerular filtration rate. For proteins independently associated with incident IS, a secondary stratified analysis evaluated associations in subgroups defined by sex and race. Exploratory analyses evaluated plasma proteomic associations with cardioembolic and noncardioembolic IS and proteins associated with IS risk in participants with left atrial dysfunction but without atrial fibrillation. RESULTS: Of 2,983 eligible participants, the mean age was 74.3 (±4.8) years, 61.2% were women, and 15.4% were Black. Over a median follow-up of 12.6 years, 450 participants experienced an incident IS. N-terminal probrain natriuretic peptide (NTproBNP, adjusted HR 1.37, 95% CI 1.23-1.53, p = 2.08 × 10-08) and macrophage metalloelastase (MMP12, adjusted HR 1.30, 95% CI 1.16-1.45, p = 4.55 × 10-06) were independently associated with IS risk. These 2 associations were similar in men and women and in Black and non-Black participants. In exploratory analyses, NTproBNP was independently associated with incident cardioembolic IS, E-selectin with incident noncardioembolic IS, and secreted frizzled-related protein 1 with IS risk in participants with left atrial dysfunction. DISCUSSION: In a cohort of older adults, NTproBNP and MMP12 were independently associated with IS risk. We identified plasma proteomic determinants of incident cardioembolic and noncardioembolic IS and found a novel protein associated with IS risk in those with left atrial dysfunction.

Metabolomic Signatures Associated With Pulmonary Arterial Hypertension Outcomes.

BACKGROUND: Pulmonary arterial hypertension (PAH) is a complex disease characterized by progressive right ventricular (RV) failure leading to significant morbidity and mortality. Investigating metabolic features and pathways associated with RV dilation, mortality, and measures of disease severity can provide insight into molecular mechanisms, identify subphenotypes, and suggest potential therapeutic targets. METHODS: We collected data from a prospective cohort of PAH participants and performed untargeted metabolomic profiling on 1045 metabolites from circulating blood. Analyses were intended to identify metabolomic differences across a range of common metrics in PAH (eg, dilated versus nondilated RV). Partial least squares discriminant analysis was first applied to assess the distinguishability of relevant outcomes. Significantly altered metabolites were then identified using linear regression, and Cox regression models (as appropriate for the specific outcome) with adjustments for age, sex, body mass index, and PAH cause. Models exploring RV maladaptation were further adjusted for pulmonary vascular resistance. Pathway enrichment analysis was performed to identify significantly dysregulated processes. RESULTS: A total of 117 participants with PAH were included. Partial least squares discriminant analysis showed cluster differentiation between participants with dilated versus nondilated RVs, survivors versus nonsurvivors, and across a range of NT-proBNP (N-terminal pro-B-type natriuretic peptide) levels, REVEAL 2.0 composite scores, and 6-minute-walk distances. Polyamine and histidine pathways were associated with differences in RV dilation, mortality, NT-proBNP, REVEAL score, and 6-minute walk distance. Acylcarnitine pathways were associated with NT-proBNP, REVEAL score, and 6-minute walk distance. Sphingomyelin pathways were associated with RV dilation and NT-proBNP after adjustment for pulmonary vascular resistance. CONCLUSIONS: Distinct plasma metabolomic profiles are associated with RV dilation, mortality, and measures of disease severity in PAH. Polyamine, histidine, and sphingomyelin metabolic pathways represent promising candidates for identifying patients at high risk for poor outcomes and investigation into their roles as markers or mediators of disease progression and RV adaptation.