Metabolomics Dynamics Study for Severe Patient

NCT ID: NCT02164786

Last Updated: 2017-04-28

Basic Information

• Recruitment Status: UNKNOWN
• Total Enrollment: 600 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2014-06-30
• Study Completion Date: 2018-12-31

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.

Conditions

Age:18-70 Years; Acute Severe Disease; Acute Physiology And Chronic Health Evaluation(APACHE)II>10

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: PROSPECTIVE

Study Groups

Acute severe disease

No interventions assigned to this group

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

• Minimum Eligible Age: 18 Years
• Maximum Eligible Age: 70 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: Yes

Sponsors

Sichuan Academy of Medical Sciences

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Principal Investigators

Hua Jiang, PhD, MBBS

Role: STUDY_CHAIR

Sichuan Academy of Medical Sciences

Locations

Sichuan Academy of Medical Sciences

Chengdu, Sichuan, China

Site Status: RECRUITING

Countries

China

Central Contacts

Hua Jiang, PhD, MBBS

Role: CONTACT

hua.jiang@traumabank.org

86-28-88424391

Bin Cai, MBBS

Role: CONTACT

bin.cai@traumabank.org

86-28-88424391

Facility Contacts

Hua Jiang, MBBS,PhD

Role: primary

cdjianghua@gmail.com

8613980001701

Bin Cai, MBBS

Role: backup

bin.cai@traumabank.org

86-18981838125

Other Identifiers

MetaLab_2014_001

Identifier Type: -

Identifier Source: org_study_id