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Discovery of Distinct Immune Phenotypes Using Machine Learning in Pulmonary Arterial Hypertension.

ABSTRACT: RATIONALE:Accumulating evidence implicates inflammation in pulmonary arterial hypertension (PAH) and therapies targeting immunity are under investigation, although it remains unknown if distinct immune phenotypes exist. OBJECTIVE:Identify PAH immune phenotypes based on unsupervised analysis of blood proteomic profiles. METHODS AND RESULTS:In a prospective observational study of group 1 PAH patients evaluated at Stanford University (discovery cohort; n=281) and University of Sheffield (validation cohort; n=104) between 2008 and 2014, we measured a circulating proteomic panel of 48 cytokines, chemokines, and factors using multiplex immunoassay. Unsupervised machine learning (consensus clustering) was applied in both cohorts independently to classify patients into proteomic immune clusters, without guidance from clinical features. To identify central proteins in each cluster, we performed partial correlation network analysis. Clinical characteristics and outcomes were subsequently compared across clusters. Four PAH clusters with distinct proteomic immune profiles were identified in the discovery cohort. Cluster 2 (n=109) had low cytokine levels similar to controls. Other clusters had unique sets of upregulated proteins central to immune networks-cluster 1 (n=58; TRAIL [tumor necrosis factor-related apoptosis-inducing ligand], CCL5 [C-C motif chemokine ligand 5], CCL7, CCL4, MIF [macrophage migration inhibitory factor]), cluster 3 (n=77; IL [interleukin]-12, IL-17, IL-10, IL-7, VEGF [vascular endothelial growth factor]), and cluster 4 (n=37; IL-8, IL-4, PDGF-? [platelet-derived growth factor beta], IL-6, CCL11). Demographics, PAH clinical subtypes, comorbidities, and medications were similar across clusters. Noninvasive and hemodynamic surrogates of clinical risk identified cluster 1 as high-risk and cluster 3 as low-risk groups. Five-year transplant-free survival rates were unfavorable for cluster 1 (47.6%; 95% CI, 35.4%-64.1%) and favorable for cluster 3 (82.4%; 95% CI, 72.0%-94.3%; across-cluster P<0.001). Findings were replicated in the validation cohort, where machine learning classified 4 immune clusters with comparable proteomic, clinical, and prognostic features. CONCLUSIONS:Blood cytokine profiles distinguish PAH immune phenotypes with differing clinical risk that are independent of World Health Organization group 1 subtypes. These phenotypes could inform mechanistic studies of disease pathobiology and provide a framework to examine patient responses to emerging therapies targeting immunity.

SUBMITTER: Sweatt AJ

PROVIDER: S-EPMC6428071 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

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Discovery of Distinct Immune Phenotypes Using Machine Learning in Pulmonary Arterial Hypertension.

Sweatt Andrew J AJ Hedlin Haley K HK Balasubramanian Vidhya V Hsi Andrew A Blum Lisa K LK Robinson William H WH Haddad Francois F Hickey Peter M PM Condliffe Robin R Lawrie Allan A Nicolls Mark R MR Rabinovitch Marlene M Khatri Purvesh P Zamanian Roham T RT

Circulation research 20190301 6

<h4>Rationale</h4>Accumulating evidence implicates inflammation in pulmonary arterial hypertension (PAH) and therapies targeting immunity are under investigation, although it remains unknown if distinct immune phenotypes exist.<h4>Objective</h4>Identify PAH immune phenotypes based on unsupervised analysis of blood proteomic profiles.<h4>Methods and results</h4>In a prospective observational study of group 1 PAH patients evaluated at Stanford University (discovery cohort; n=281) and University of ...[more]

PMID: 30661465

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Project description:Women with pulmonary arterial hypertension (PAH) experience multiple symptoms, including dyspnea, fatigue, and sleep disturbance, that impair their health-related quality of life (HRQOL). However, we know little about phenotypic subgroups of patients with PAH with similar, concurrent, multiple symptoms. The objectives of this study were to define the "symptome" by symptom cluster phenotypes and compare characteristics such as biomarkers, cardiac structure and function (echocardiography), functional capacity (6-min walk distance), and HRQOL between the groups. This cross-sectional study included 60 women with PAH. Subjects completed an assessment battery: Pulmonary Arterial Hypertension Symptom Scale, Pittsburgh Sleep Quality Index, Multidimensional Dyspnea Profile, Patient-Reported Outcomes Measurement Information System (PROMIS®) Physical Function, PROMIS® Sleep-Related Impairment, and the emPHasis-10. Subjects also underwent transthoracic echocardiography, phlebotomy, 6-min walk distance, and actigraphy. The three symptoms of dyspnea, fatigue, and sleep disturbance were used to define the symptom clusters. Other PAH symptoms, plasma and serum biomarkers, cardiac structure and function (echocardiography), exercise capacity (6-min walk distance), sleep (actigraphy), and HRQOL were compared across phenotypes. The mean age was 50 ± 18 years, 51% were non-Hispanic white, 32% were non-Hispanic Black and 40% had idiopathic PAH. Cluster analysis identified Mild (n = 28, 47%), Moderate (n = 20, 33%), and Severe Symptom Cluster Phenotypes (n = 12, 20%). There were no differences for age, race, or PAH etiology between the phenotypes. WHO functional class (p < 0.001), norepinephrine levels (p = 0.029), right atrial pressure (p = 0.001), physical function (p < 0.001), sleep onset latency (p = 0.040), and HRQOL (p < 0.001) all differed significantly across phenotypes. We identified three distinctive symptom cluster phenotypes (Mild, Moderate, and Severe) for women with PAH that also differed by PAH-related symptoms, physical function, right atrial pressure, norepinephrine levels, and HRQOL. These phenotypes could suggest targeted interventions to improve symptoms and HRQOL in those most severely affected.

Project description:BackgroundCardiogenic shock (CS) is a complex state with many underlying causes and associated outcomes. It is still difficult to differentiate between various CS phenotypes. We investigated if the CS phenotypes with distinctive clinical profiles and prognoses might be found using the machine learning (ML) consensus clustering approach.MethodsThe current study included patients who were diagnosed with CS at the time of admission from the electronic ICU (eICU) Collaborative Research Database. Among 21,925 patients with CS, an unsupervised ML consensus clustering analysis was conducted. The optimal number of clusters was identified by means of the consensus matrix (CM) heat map, cumulative distribution function (CDF), cluster-consensus plots, and the proportion of ambiguously clustered pairs (PAC) analysis. We calculated the standardized mean difference (SMD) of each variable and used the cutoff of ± 0.3 to identify each cluster's key features. We examined the relationship between the phenotypes and several clinical endpoints utilizing logistic regression (LR) analysis.ResultsThe consensus cluster analysis identified two clusters (Cluster 1: n = 9,848; Cluster 2: n = 12,077). The key features of patients in Cluster 1, compared with Cluster 2, included: lower blood pressure, lower eGFR (estimated glomerular filtration rate), higher BUN (blood urea nitrogen), higher creatinine, lower albumin, higher potassium, lower bicarbonate, lower red blood cell (RBC), higher red blood cell distribution width (RDW), higher SOFA score, higher APS III score, and higher APACHE IV score on admission. The results of LR analysis showed that the Cluster 2 was associated with lower in-hospital mortality (odds ratio [OR]: 0.374; 95% confidence interval [CI]: 0.347-0.402; P < 0.001), ICU mortality (OR: 0.349; 95% CI: 0.318-0.382; P < 0.001), and the incidence of acute kidney injury (AKI) after admission (OR: 0.478; 95% CI: 0.452-0.505; P < 0.001).ConclusionsML consensus clustering analysis synthesized the pattern of clinical and laboratory data to reveal distinct CS phenotypes with different clinical outcomes.

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Discovery of Distinct Immune Phenotypes Using Machine Learning in Pulmonary Arterial Hypertension.

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Discovery of Distinct Immune Phenotypes Using Machine Learning in Pulmonary Arterial Hypertension.

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