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Circulating cell-free hemoglobin (CFH) is elevated in pulmonary arterial hypertension (PAH) and associated with poor outcomes but the mechanisms are unknown. We hypothesized that CFH is generated from the pulmonary circulation and inadequately cleared in PAH. Transpulmonary CFH (difference between wedge and pulmonary artery positions) and lung hemoglobin α were analyzed in patients with PAH and healthy controls. Haptoglobin genotype and plasma hemoglobin processing proteins were analyzed in patients with PAH, unaffected bone morphogenetic protein receptor type II mutation carriers (UMCs), and control subjects. Transpulmonary CFH was increased in patients with PAH (p = 0.04) and correlated with pulmonary vascular resistanc (PVR) (r s = 0.75, p = 0.02) and mean pulmonary arterial pressure (mPAP) (r s = 0.78, p = 0.02). Pulmonary vascular hemoglobin α protein was increased in patients with PAH (p = 0.006), especially in occluded vessels (p = 0.04). Haptoglobin genotype did not differ between groups. Plasma haptoglobin was higher in UMCs compared with both control subjects (p = 0.03) and patients with HPAH (p < 0.0001); patients with IPAH had higher circulating haptoglobin levels than patients with HPAH (p = 0.006). Notably, circulating CFH to haptoglobin ratio was elevated in patients with HPAH compared to control subjects (p = 0.02) and UMCs (p = 0.006). Moreover, in patients with PAH, CFH: haptoglobin correlated with PVR (r s = 0.37, p = 0.0004) and mPAP (r s = 0.25, p = 0.02). Broad alterations in other plasma hemoglobin processing proteins (hemopexin, heme oxygenase-1, and sCD163) were observed. In conclusion, pulmonary vascular CFH is associated with increased PVR and mPAP in PAH and dysregulated CFH clearance may contribute to PAH pathology. Further study is needed to determine whether targeting CFH is a viable therapeutic for pulmonary vascular dysfunction in PAH.
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OBJECTIVE: COVID-19 survivors are at risk for long-term health effects, but assessing the sequelae of COVID-19 at large scales is challenging. High-throughput methods to efficiently identify new medical problems arising after acute medical events using the electronic health record (EHR) could improve surveillance for long-term consequences of acute medical problems like COVID-19. MATERIALS AND METHODS: We augmented an existing high-throughput phenotyping method (PheWAS) to identify new diagnoses occurring after an acute temporal event in the EHR. We then used the temporal-informed phenotypes to assess development of new medical problems among COVID-19 survivors enrolled in an EHR cohort of adults tested for COVID-19 at Vanderbilt University Medical Center. RESULTS: The study cohort included 186â105 adults tested for COVID-19 from March 5, 2020 to November 1, 2021; of which 30â088 (16.2%) tested positive. Median follow-up after testing was 412 days (IQR 274-528). Our temporal-informed phenotyping was able to distinguish phenotype chapters based on chronicity of their constituent diagnoses. PheWAS with temporal-informed phenotypes identified increased risk for 43 diagnoses among COVID-19 survivors during outpatient follow-up, including multiple new respiratory, cardiovascular, neurological, and pregnancy-related conditions. Findings were robust to sensitivity analyses, and several phenotypic associations were supported by changes in outpatient vital signs or laboratory tests from the pretesting to postrecovery period. CONCLUSION: Temporal-informed PheWAS identified new diagnoses affecting multiple organ systems among COVID-19 survivors. These findings can inform future efforts to enable longitudinal health surveillance for survivors of COVID-19 and other acute medical conditions using the EHR.
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COVID-19 , Humanos , Fenotipo , Registros Electrónicos de SaludRESUMEN
Over 200 million SARS-CoV-2 patients have or will develop persistent symptoms (long COVID). Given this pressing research priority, the National COVID Cohort Collaborative (N3C) developed a machine learning model using only electronic health record data to identify potential patients with long COVID. We hypothesized that additional data from health surveys, mobile devices, and genotypes could improve prediction ability. In a cohort of SARS-CoV-2 infected individuals (n=17,755) in the All of Us program, we applied and expanded upon the N3C long COVID prediction model, testing machine learning infrastructures, assessing model performance, and identifying factors that contributed most to the prediction models. For the survey/mobile device information and genetic data, extreme gradient boosting and a convolutional neural network delivered the best performance for predicting long COVID, respectively. Combined survey, genetic, and mobile data increased specificity and the Area Under Curve the Receiver Operating Characteristic score versus the original N3C model.
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Adipocyte iron overload is a maladaptation associated with obesity and insulin resistance. The objective of the current study was to determine whether and how adipose tissue macrophages (ATMs) regulate adipocyte iron concentrations and whether this is impacted by obesity. Using bone marrow-derived macrophages (BMDMs) polarized to M0, M1, M2, or metabolically activated (MMe) phenotypes, we showed that MMe BMDMs and ATMs from obese mice have reduced expression of several iron-related proteins. Furthermore, the bioenergetic response to iron in obese ATMs was hampered. ATMs from iron-injected lean mice increased their glycolytic and respiratory capacities, thus maintaining metabolic flexibility, while ATMs from obese mice did not. Using an isotope-based system, we found that iron exchange between BMDMs and adipocytes was regulated by macrophage phenotype. At the end of the co-culture, MMe macrophages transferred and received more iron from adipocytes than M0, M1, and M2 macrophages. This culminated in a decrease in total iron in MMe macrophages and an increase in total iron in adipocytes compared with M2 macrophages. Taken together, in the MMe condition, the redistribution of iron is biased toward macrophage iron deficiency and simultaneous adipocyte iron overload. These data suggest that obesity changes the communication of iron between adipocytes and macrophages and that rectifying this iron communication channel may be a novel therapeutic target to alleviate insulin resistance.
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Resistencia a la Insulina , Sobrecarga de Hierro , Adipocitos/metabolismo , Tejido Adiposo/metabolismo , Animales , Inflamación/metabolismo , Hierro/metabolismo , Sobrecarga de Hierro/metabolismo , Macrófagos/metabolismo , Ratones , Ratones Obesos , Obesidad/metabolismo , FenotipoRESUMEN
Discovering novel uses for existing drugs, through drug repurposing, can reduce the time, costs, and risk of failure associated with new drug development. However, prioritizing drug repurposing candidates for downstream studies remains challenging. Here, we present a high-throughput approach to identify and validate drug repurposing candidates. This approach integrates human gene expression, drug perturbation, and clinical data from publicly available resources. We apply this approach to find drug repurposing candidates for two diseases, hyperlipidemia and hypertension. We screen >21,000 compounds and replicate ten approved drugs. We also identify 25 (seven for hyperlipidemia, eighteen for hypertension) drugs approved for other indications with therapeutic effects on clinically relevant biomarkers. For five of these drugs, the therapeutic effects are replicated in the All of Us Research Program database. We anticipate our approach will enable researchers to integrate multiple publicly available datasets to identify high priority drug repurposing opportunities for human diseases.
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Reposicionamiento de Medicamentos , Expresión Génica , Hiperlipidemias , Hipertensión , Biología Computacional , Bases de Datos Factuales , Ensayos Analíticos de Alto Rendimiento , Humanos , Preparaciones Farmacéuticas , Salud PoblacionalRESUMEN
OBJECTIVE: Identifying symptoms and characteristics highly specific to coronavirus disease 2019 (COVID-19) would improve the clinical and public health response to this pandemic challenge. Here, we describe a high-throughput approach - Concept-Wide Association Study (ConceptWAS) - that systematically scans a disease's clinical manifestations from clinical notes. We used this method to identify symptoms specific to COVID-19 early in the course of the pandemic. METHODS: We created a natural language processing pipeline to extract concepts from clinical notes in a local ER corresponding to the PCR testing date for patients who had a COVID-19 test and evaluated these concepts as predictors for developing COVID-19. We identified predictors from Firth's logistic regression adjusted by age, gender, and race. We also performed ConceptWAS using cumulative data every two weeks to identify the timeline for recognition of early COVID-19-specific symptoms. RESULTS: We processed 87,753 notes from 19,692 patients subjected to COVID-19 PCR testing between March 8, 2020, and May 27, 2020 (1,483 COVID-19-positive). We found 68 concepts significantly associated with a positive COVID-19 test. We identified symptoms associated with increasing risk of COVID-19, including "anosmia" (odds ratio [OR] = 4.97, 95% confidence interval [CI] = 3.21-7.50), "fever" (OR = 1.43, 95% CI = 1.28-1.59), "cough with fever" (OR = 2.29, 95% CI = 1.75-2.96), and "ageusia" (OR = 5.18, 95% CI = 3.02-8.58). Using ConceptWAS, we were able to detect loss of smell and loss of taste three weeks prior to their inclusion as symptoms of the disease by the Centers for Disease Control and Prevention (CDC). CONCLUSION: ConceptWAS, a high-throughput approach for exploring specific symptoms and characteristics of a disease like COVID-19, offers a promise for enabling EHR-powered early disease manifestations identification.
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COVID-19/diagnóstico , Procesamiento de Lenguaje Natural , Evaluación de Síntomas/métodos , Adulto , Ageusia , Prueba de Ácido Nucleico para COVID-19 , Tos , Femenino , Fiebre , Humanos , Masculino , Persona de Mediana Edad , Pandemias , Estados UnidosRESUMEN
Objective: Identifying symptoms highly specific to COVID-19 would improve the clinical and public health response to infectious outbreaks. Here, we describe a high-throughput approach - Concept-Wide Association Study (ConceptWAS) that systematically scans a disease's clinical manifestations from clinical notes. We used this method to identify symptoms specific to COVID-19 early in the course of the pandemic. Methods: Using the Vanderbilt University Medical Center (VUMC) EHR, we parsed clinical notes through a natural language processing pipeline to extract clinical concepts. We examined the difference in concepts derived from the notes of COVID-19-positive and COVID-19-negative patients on the PCR testing date. We performed ConceptWAS using the cumulative data every two weeks for early identifying specific COVID-19 symptoms. Results: We processed 87,753 notes 19,692 patients (1,483 COVID-19-positive) subjected to COVID-19 PCR testing between March 8, 2020, and May 27, 2020. We found 68 clinical concepts significantly associated with COVID-19. We identified symptoms associated with increasing risk of COVID-19, including "absent sense of smell" (odds ratio [OR] = 4.97, 95% confidence interval [CI] = 3.21-7.50), "fever" (OR = 1.43, 95% CI = 1.28-1.59), "with cough fever" (OR = 2.29, 95% CI = 1.75-2.96), and "ageusia" (OR = 5.18, 95% CI = 3.02-8.58). Using ConceptWAS, we were able to detect loss sense of smell or taste three weeks prior to their inclusion as symptoms of the disease by the Centers for Disease Control and Prevention (CDC). Conclusion: ConceptWAS is a high-throughput approach for exploring specific symptoms of a disease like COVID-19, with a promise for enabling EHR-powered early disease manifestations identification.
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Dolor Agudo/etiología , Anemia de Células Falciformes/genética , Estudios de Asociación Genética , Haptoglobinas/genética , Síndrome Torácico Agudo/epidemiología , Síndrome Torácico Agudo/etiología , Dolor Agudo/epidemiología , Adolescente , Alelos , Anemia de Células Falciformes/complicaciones , Arteriopatías Oclusivas/etiología , Niño , Preescolar , Frecuencia de los Genes , Predisposición Genética a la Enfermedad , Hemo/análisis , Humanos , Incidencia , Estudios Prospectivos , Riesgo , Adulto JovenRESUMEN
OBJECTIVE: Discovering subphenotypes of complex diseases can help characterize disease cohorts for investigative studies aimed at developing better diagnoses and treatments. Recent advances in unsupervised machine learning on electronic health record (EHR) data have enabled researchers to discover phenotypes without input from domain experts. However, most existing studies have ignored time and modeled diseases as discrete events. Uncovering the evolution of phenotypes - how they emerge, evolve and contribute to health outcomes - is essential to define more precise phenotypes and refine the understanding of disease progression. Our objective was to assess the benefits of an unsupervised approach that incorporates time to model diseases as dynamic processes in phenotype discovery. METHODS: In this study, we applied a constrained non-negative tensor-factorization approach to characterize the complexity of cardiovascular disease (CVD) patient cohort based on longitudinal EHR data. Through tensor-factorization, we identified a set of phenotypic topics (i.e., subphenotypes) that these patients established over the 10â¯years prior to the diagnosis of CVD, and showed the progress pattern. For each identified subphenotype, we examined its association with the risk for adverse cardiovascular outcomes estimated by the American College of Cardiology/American Heart Association Pooled Cohort Risk Equations, a conventional CVD-risk assessment tool frequently used in clinical practice. Furthermore, we compared the subsequent myocardial infarction (MI) rates among the six most prevalent subphenotypes using survival analysis. RESULTS: From a cohort of 12,380 adult CVD individuals with 1068 unique PheCodes, we successfully identified 14 subphenotypes. Through the association analysis with estimated CVD risk for each subtype, we found some phenotypic topics such as Vitamin D deficiency and depression, Urinary infections cannot be explained by the conventional risk factors. Through a survival analysis, we found markedly different risks of subsequent MI following the diagnosis of CVD among the six most prevalent topics (pâ¯<â¯0.0001), indicating these topics may capture clinically meaningful subphenotypes of CVD. CONCLUSION: This study demonstrates the potential benefits of using tensor-decomposition to model diseases as dynamic processes from longitudinal EHR data. Our results suggest that this data-driven approach may potentially help researchers identify complex and chronic disease subphenotypes in precision medicine research.
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Enfermedades Cardiovasculares/diagnóstico , Registros Electrónicos de Salud , Informática Médica/métodos , Centros Médicos Académicos , Algoritmos , Bases de Datos Factuales , Humanos , Infarto del Miocardio/complicaciones , Fenotipo , Medicina de Precisión , Riesgo , Factores de Riesgo , Sociedades Médicas , Estados Unidos , Aprendizaje Automático no Supervisado , Infecciones Urinarias/complicaciones , Infecciones Urinarias/diagnóstico , Deficiencia de Vitamina D/complicacionesRESUMEN
BACKGROUND: Alcohol abuse increases the risk for acute lung injury (ALI). In both experimental models and in clinical studies, chronic alcohol ingestion causes airway oxidative stress and glutathione depletion and increases the expression of transforming growth factor beta-1 (TGFß1), a potent inducer of fibrosis, in the lung. Therefore, we hypothesized that alcohol ingestion could promote aberrant fibrosis following experimental ALI and that treatment with the glutathione precursor s-adenosylmethionine (SAMe) could mitigate these effects. METHODS: Three-month-old C57BL/6 mice were fed standard chow ± alcohol (20% v/v) in their drinking water for 8 weeks and ±SAMe (4% w/v) during the last 4 weeks. ALI was induced by intratracheal instillation of bleomycin (2.5 units/kg), and lungs were assessed histologically at 7 and 14 days for fibrosis and at 14 days for the expression of extracellular matrix proteins and TGFß1. RESULTS: Alcohol ingestion had no apparent effect on lung inflammation at 7 days, but at 14 days after bleomycin treatment, it increased lung tissue collagen deposition, hydroxyproline content, and the release of activated TGFß1 into the airway. In contrast, SAMe supplementation completely mitigated alcohol-induced priming of these aberrant fibrotic changes through decreased TGFß1 expression in the lung. In parallel, SAMe decreased alcohol-induced TGFß1 and Smad3 mRNA expressions by lung fibroblasts in vitro. CONCLUSIONS: These new experimental findings demonstrate that chronic alcohol ingestion renders the experimental mouse lung susceptible to fibrosis following bleomycin-induced ALI, and that these effects are likely driven by alcohol-mediated oxidative stress and its induction and activation of TGFß1.