Dataset Information

Early prediction of phenotypic severity in Citrullinemia Type 1.

ABSTRACT: Objective: Citrullinemia type 1 (CTLN1) is an inherited metabolic disease affecting the brain which is detectable by newborn screening. The clinical spectrum is highly variable including individuals with lethal hyperammonemic encephalopathy in the newborn period and individuals with a mild-to-moderate or asymptomatic disease course. Since the phenotypic severity has not been predictable early during the disease course so far, we aimed to design a reliable disease prediction model.

Methods: We used a newly established mammalian biallelic expression system to determine residual enzymatic activity of argininosuccinate synthetase 1 (ASS1; OMIM #215700) in 71 individuals with CTLN1, representing 48 ASS1 gene variants and 50 different, mostly compound heterozygous combinations in total. Residual enzymatic ASS1 activity was correlated to standardized biochemical and clinical endpoints available from the UCDC and E-IMD databases.

Results: Residual enzymatic ASS1 activity correlates with peak plasma ammonium and L-citrulline concentrations at initial presentation. Individuals with 8% of residual enzymatic ASS1 activity or less had more frequent and more severe hyperammonemic events and lower cognitive function than those above 8%, highlighting that residual enzymatic ASS1 activity allows reliable severity prediction. Noteworthy, empiric clinical practice of affected individuals is in line with the predicted disease severity supporting the notion of a risk stratification-based guidance of therapeutic decision-making based on residual enzymatic ASS1 activity in the future.

Interpretation: Residual enzymatic ASS1 activity reliably predicts the phenotypic severity in CTLN1. We propose a new severity-adjusted classification system for individuals with CTLN1 based on the activity results of the newly established biallelic expression system.

SUBMITTER: Zielonka M

PROVIDER: S-EPMC6764635 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Early prediction of phenotypic severity in Citrullinemia Type 1.

Zielonka Matthias M Kölker Stefan S Gleich Florian F Stützenberger Nicolas N Nagamani Sandesh C S SCS Gropman Andrea L AL Hoffmann Georg F GF Garbade Sven F SF Posset Roland R

Annals of clinical and translational neurology 20190830 9

<h4>Objective</h4>Citrullinemia type 1 (CTLN1) is an inherited metabolic disease affecting the brain which is detectable by newborn screening. The clinical spectrum is highly variable including individuals with lethal hyperammonemic encephalopathy in the newborn period and individuals with a mild-to-moderate or asymptomatic disease course. Since the phenotypic severity has not been predictable early during the disease course so far, we aimed to design a reliable disease prediction model.<h4>Meth ...[more]

PMID: 31469252

Similar Datasets

Project description:BackgroundThe majority of U.S. reports of COVID-19 clinical characteristics, disease course, and treatments are from single health systems or focused on one domain. Here we report the creation of the National COVID Cohort Collaborative (N3C), a centralized, harmonized, high-granularity electronic health record repository that is the largest, most representative U.S. cohort of COVID-19 cases and controls to date. This multi-center dataset supports robust evidence-based development of predictive and diagnostic tools and informs critical care and policy.Methods and findingsIn a retrospective cohort study of 1,926,526 patients from 34 medical centers nationwide, we stratified patients using a World Health Organization COVID-19 severity scale and demographics; we then evaluated differences between groups over time using multivariable logistic regression. We established vital signs and laboratory values among COVID-19 patients with different severities, providing the foundation for predictive analytics. The cohort included 174,568 adults with severe acute respiratory syndrome associated with SARS-CoV-2 (PCR >99% or antigen <1%) as well as 1,133,848 adult patients that served as lab-negative controls. Among 32,472 hospitalized patients, mortality was 11.6% overall and decreased from 16.4% in March/April 2020 to 8.6% in September/October 2020 (p = 0.002 monthly trend). In a multivariable logistic regression model, age, male sex, liver disease, dementia, African-American and Asian race, and obesity were independently associated with higher clinical severity. To demonstrate the utility of the N3C cohort for analytics, we used machine learning (ML) to predict clinical severity and risk factors over time. Using 64 inputs available on the first hospital day, we predicted a severe clinical course (death, discharge to hospice, invasive ventilation, or extracorporeal membrane oxygenation) using random forest and XGBoost models (AUROC 0.86 and 0.87 respectively) that were stable over time. The most powerful predictors in these models are patient age and widely available vital sign and laboratory values. The established expected trajectories for many vital signs and laboratory values among patients with different clinical severities validates observations from smaller studies, and provides comprehensive insight into COVID-19 characterization in U.S. patients.ConclusionsThis is the first description of an ongoing longitudinal observational study of patients seen in diverse clinical settings and geographical regions and is the largest COVID-19 cohort in the United States. Such data are the foundation for ML models that can be the basis for generalizable clinical decision support tools. The N3C Data Enclave is unique in providing transparent, reproducible, easily shared, versioned, and fully auditable data and analytic provenance for national-scale patient-level EHR data. The N3C is built for intensive ML analyses by academic, industry, and citizen scientists internationally. Many observational correlations can inform trial designs and care guidelines for this new disease.

Project description:Introduction Sepsis can lead to organ dysfunctions with disturbed oxygen dynamics and life-threatening consequences. Since the results of organ-protective treatments cannot always be transferred from laboratory models into human therapies, increasing the translational potential of preclinical settings is an important goal. Our aim was to develop a standardized research protocol, where the progression of sepsis-related events can be characterized reproducibly in model experiments within clinically-relevant time frames. Methods Peritonitis was induced in anesthetized minipigs injected intraperitoneally with autofeces inoculum (n = 27) or with saline (sham operation; n = 9). The microbial colony-forming units (CFUs) in the inoculum were retrospectively determined. After awakening, clinically relevant supportive therapies were conducted. Nineteen inoculated animals developed sepsis without a fulminant reaction. Sixteen hours later, these animals were re-anesthetized for invasive monitoring. Blood samples were taken to detect plasma TNF-α, IL-10, big endothelin (bET), high mobility group box protein1 (HMGB1) levels and blood gases, and sublingual microcirculatory measurements were conducted. Hemodynamic, respiratory, coagulation, liver and kidney dysfunctions were detected to characterize the septic status with a pig-specific Sequential Organ Failure Assessment (pSOFA) score and its simplified version (respiratory, cardiovascular and renal failure) between 16 and 24 h of the experiments. Results Despite the standardized sepsis induction, the animals could be clustered into two distinct levels of severity: a sepsis (n = 10; median pSOFA score = 2) and a septic shock (n = 9; median pSOFA score = 8) subgroup at 18 h of the experiments, when the decreased systemic vascular resistance, increased DO2 and VO2, and markedly increased ExO2 demonstrated a compensated hyperdynamic state. Septic animals showed severity-dependent scores for organ failure with reduced microcirculation despite the adequate oxygen dynamics. Sepsis severity characterized later with pSOFA scores was in correlation with the germ count in the induction inoculum (r = 0.664) and CFUs in hemocultures (r = 0.876). Early changes in plasma levels of TNF-α, bET and HMGB1 were all related to the late-onset organ dysfunctions characterized by pSOFA scores. Conclusions This microbiologically-monitored, large animal model of intraabdominal sepsis is suitable for clinically-relevant investigations. The methodology combines the advantages of conscious and anesthetized studies, and mimics human sepsis and septic shock closely with the possibility of numerical quantification of host responses.

Dataset Information

Early prediction of phenotypic severity in Citrullinemia Type 1.

Publications

Early prediction of phenotypic severity in Citrullinemia Type 1.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets