5 research outputs found

    Characterization of long COVID temporal sub-phenotypes by distributed representation learning from electronic health record data: a cohort studyResearch in Context

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    Summary: Background: Characterizing Post-Acute Sequelae of COVID (SARS-CoV-2 Infection), or PASC has been challenging due to the multitude of sub-phenotypes, temporal attributes, and definitions. Scalable characterization of PASC sub-phenotypes can enhance screening capacities, disease management, and treatment planning. Methods: We conducted a retrospective multi-centre observational cohort study, leveraging longitudinal electronic health record (EHR) data of 30,422 patients from three healthcare systems in the Consortium for the Clinical Characterization of COVID-19 by EHR (4CE). From the total cohort, we applied a deductive approach on 12,424 individuals with follow-up data and developed a distributed representation learning process for providing augmented definitions for PASC sub-phenotypes. Findings: Our framework characterized seven PASC sub-phenotypes. We estimated that on average 15.7% of the hospitalized COVID-19 patients were likely to suffer from at least one PASC symptom and almost 5.98%, on average, had multiple symptoms. Joint pain and dyspnea had the highest prevalence, with an average prevalence of 5.45% and 4.53%, respectively. Interpretation: We provided a scalable framework to every participating healthcare system for estimating PASC sub-phenotypes prevalence and temporal attributes, thus developing a unified model that characterizes augmented sub-phenotypes across the different systems. Funding: Authors are supported by National Institute of Allergy and Infectious Diseases, National Institute on Aging, National Center for Advancing Translational Sciences, National Medical Research Council, National Institute of Neurological Disorders and Stroke, European Union, National Institutes of Health, National Center for Advancing Translational Sciences

    Heterozygous loss-of-function variants significantly expand the phenotypes associated with loss of GDF11

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    Growth differentiation factor 11 (GDF11) is a key signaling protein required for proper development of many organ systems. Only one prior study has associated an inherited GDF11 variant with a dominant human disease in a family with variable craniofacial and vertebral abnormalities. Here, we expand the phenotypic spectrum associated with GDF11 variants and document the nature of the variants.We present a cohort of six probands with de novo and inherited nonsense/frameshift (4/6 patients) and missense (2/6) variants in GDF11. We generated gdf11 mutant zebrafish to model loss of gdf11 phenotypes and used an overexpression screen in Drosophila to test variant functionality.Patients with variants in GDF11 presented with craniofacial (5/6), vertebral (5/6), neurological (6/6), visual (4/6), cardiac (3/6), auditory (3/6), and connective tissue abnormalities (3/6). gdf11 mutant zebrafish show craniofacial abnormalities and body segmentation defects that match some patient phenotypes. Expression of the patients’ variants in the fly showed that one nonsense variant in GDF11 is a severe loss-of-function (LOF) allele whereas the missense variants in our cohort are partial LOF variants.GDF11 is needed for human development, particularly neuronal development, and LOF GDF11 alleles can affect the development of numerous organs and tissues
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