Clinical trial NCT02164786: Metabolomics Dynamics Study for Severe Patient
Official title: Modeling Metabolomic Dynamics Based on Nuclear Magnetic Resonance and High Performance Liquid Chromatography for Severe Patient: a Cohort Study
Study type: OBSERVATIONAL
Overall status: UNKNOWN
Conditions: Age:18-70 Years
Acute Severe Disease
Acute Physiology And Chronic Health Evaluation(APACHE)II>10
Keywords: Acute severe disease
Intensive care unit
adult
cohort study
metabolomics
NMR
High Performance Liquid Chromatography
Lead sponsor: Sichuan Academy of Medical Sciences
Sponsor class: OTHER
First posted: 2014-06-17
Enrollment: 600
Sex: ALL
Minimum age (years): 18
Maximum age (years): 70
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Patients suffering from acute severe disease

Brief summary: Acute severe disease is a major public health challenge that often affects young adults.In past decade, there are lot of new techniques have been developed that aim to improve the outcome of acute severe disease, But few of these works success. According to recently studies, the mortality of the multiple organ dysfunction syndrome（MODS) that is the major cause of death in patients who suffering from acute severe disease, is not improved. On the contrary, if MODS be predicted in early stage of acute severe disease, the death can be prevented. Because acute severe disease poses complex injury that involves multiple pathological processes, understanding the cellular and metabolic network malfunction during acute severe disease is crucial for clinical monitoring and intervention.

Human metabolism is a complex network with hundreds of cross-linked paths. During critical illness, the metabolic network is dynamically disturbed at multiple points. Classical research typically isolates a small part of this network to investigate the impact of pathological physiology molecular mechanisms on clinical outcome. In particular, researchers have examined metabolic disturbances such as cytokine network dysfunction, skeletal muscle breakdown, insulin resistance, dyslipidemia, testosterone and growth hormone/Insulin like growth factor （IGF）dysfunctions, low thyroxine syndrome, and deficiency of vitamin D and calcium with secondary hyperparathyroidism. These complex metabolic disturbances appear and interact at different stages during the pathological process after acute severe illness. Therefore, an integrated approach that combines the biochemical/molecular changes with network disturbances is the key to understanding acute severe illness at the systems biology level and establishing an accurate quantitative model for clinical monitoring.

An interdisciplinary method that includes high-throughput quantitative techniques and effective mathematical and visualization tools is necessary. Furthermore, interdisciplinary methods present the opportunity to develop innovative clinical diagnosis and monitoring methods for severe injuries. The aim of this study is to provide a novel high-throughput method that integrated proton-nuclear magnetic resonance (NMR) metabolomic fingerprinting and High Performance Liquid Chromatography with advance mathematics tools to modeling metabolic dynamics after acute severe disease.


Eligibility criteria: Inclusion Criteria:

* Age:18-70 years
* Acute Physiology And Chronic Health Evaluation(APACHE)II\>10

Exclusion Criteria:

* With comorbidity (Diabetes,Hyperthyroidism or primary organ dysfunction )
* Pregnancy

Primary outcomes:
measure: Mortality at hospitalization
timeFrame: Death events from admission to discharge(up to 10 weeks)
Secondary outcomes:
measure: Multi Organ Dysfunction Syndrome(MODS)
timeFrame: MODS events occurence from admission to discharge(up to 10 weeks)
Other outcomes:
Locations:
city: Chengdu
country: China
facility: Sichuan Academy of Medical Sciences
state: Sichuan
Study officials:
affiliation: Sichuan Academy of Medical Sciences
name: Hua Jiang, PhD, MBBS
role: STUDY_CHAIR
References: