Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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UNKNOWN
4000 participants
OBSERVATIONAL
2017-07-01
2020-12-31
Brief Summary
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Detailed Description
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The biological network using cross-omics analysis was reconstructed to identify potential causative key genes, bacteria, and endogenous metabolite targets that cause differences in individual responses. A machine identification algorithm selecting clinical factors and multi-omics targets was used to establish a predictive mathematical model.
A multi-center clinical cohort of 3000 coronal heart disease patients was used to verify the effects of various levels of omic targets on drug blood exposures, efficacy and toxic side effects. A comprehensive model based on multi-target combination of individualized drugs was constructed, and the predictive effect was clinically analyzed.
Conditions
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Study Design
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COHORT
PROSPECTIVE
Study Groups
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Discovery cohort
1000 cases of coronary heart disease follow-up cohort was used for multi-omics target discovery.During the follow-up period, the information about the occurrence and risk factors of adverse cardiovascular events will be collected.
risk factors of adverse cardiovascular events
During the follow-up period,general information(age, sex, BMI, blood pressure, the history of drink and smoke, medical history, etc).Blood biochemistry parameters（Lipid, hsCRP levels, etc）and other laboratory examination parameters will be collected
multi-omics target discovery
Genome-wide genotype , DNA methylation and metabolomes were determined using illumina high-density genotyping chips, high-throughput sequencing, and high-resolution mass spectrometry respectly. Blood exposure of statins and metoprolol and its metabolites was determined by UPLC-MS/MS.
Validation corhort
3000 coronary heart disease follow-up cohorts was used for validating the results from the discovery corhort. During the follow-up period, the occurrence and risk factors of adverse cardiovascular events.Predictive mathematical models based on multi-omics combination will be constructed finally.
risk factors of adverse cardiovascular events
During the follow-up period,general information(age, sex, BMI, blood pressure, the history of drink and smoke, medical history, etc).Blood biochemistry parameters（Lipid, hsCRP levels, etc）and other laboratory examination parameters will be collected
validation
The genome-wide genotype of patients with coronary heart disease was detected using the illumina chip. The methylation level of the functional region was detected by the target region enrichment methylation sequencing method. Intestinal flora differences were detected using 16SrDNA high-throughput sequencing.
Predictive mathematical models
Machine learning algorithms such as multiple linear regression or Bayesian classification are used to optimize clinical factors and multi-group targets to establish predictive mathematical models.
Interventions
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risk factors of adverse cardiovascular events
During the follow-up period,general information(age, sex, BMI, blood pressure, the history of drink and smoke, medical history, etc).Blood biochemistry parameters（Lipid, hsCRP levels, etc）and other laboratory examination parameters will be collected
multi-omics target discovery
Genome-wide genotype , DNA methylation and metabolomes were determined using illumina high-density genotyping chips, high-throughput sequencing, and high-resolution mass spectrometry respectly. Blood exposure of statins and metoprolol and its metabolites was determined by UPLC-MS/MS.
validation
The genome-wide genotype of patients with coronary heart disease was detected using the illumina chip. The methylation level of the functional region was detected by the target region enrichment methylation sequencing method. Intestinal flora differences were detected using 16SrDNA high-throughput sequencing.
Predictive mathematical models
Machine learning algorithms such as multiple linear regression or Bayesian classification are used to optimize clinical factors and multi-group targets to establish predictive mathematical models.
Eligibility Criteria
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Inclusion Criteria
* Chinese Han patients with coronary artery disease
* inpatients undergoing coronary angiography or percutaneous coronary intervention
Exclusion Criteria
* hepatic insufficiency (defined as serum transaminase concentration \> 2 times the upper limit of normal \[80 U/L\], or a diagnosis of cirrhosis)
* pre-existing bleeding disorders
* being pregnant or lactating
* advanced cancer or haemodialysis
* history of thyroid problems, and use of antithyroid drugs or thyroid hormone medication
* incomplete information about cardiovascular events during follow-up
18 Years
80 Years
ALL
No
Sponsors
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RenJi Hospital
OTHER
West China Hospital
OTHER
Xiangya Hospital of Central South University
OTHER
First Affiliated Hospital, Sun Yat-Sen University
OTHER
Guangdong Provincial People's Hospital
OTHER
Responsible Party
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ShiLong Zhong
Professor
Principal Investigators
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Shilong Zhong, Ph.D
Role: PRINCIPAL_INVESTIGATOR
Guangdong Provincial People's Hospital
Locations
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Guangdong General Hospital
Guangzhou, Guangdong, China
The First Affiliated Hospital of Sun Yat-sen University
Guangzhou, Guangdong, China
XiangYa Hospital Central South University
Changsha, Hunan, China
Renji Hospital Affiliated to Shanghai Jiaotong University
Shanghai, Shanghai Municipality, China
West China Hospital, Sichuan University
Chengdu, Sichuan, China
Countries
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Central Contacts
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Facility Contacts
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References
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Chen Y, Jiang H, Zhan Z, Lu J, Gu T, Yu P, Liang W, Zhang X, Liu S, Bi H, Zhong S, Tang L. Restoration of lipid homeostasis between TG and PE by the LXRalpha-ATGL/EPT1 axis ameliorates hepatosteatosis. Cell Death Dis. 2023 Feb 6;14(2):85. doi: 10.1038/s41419-023-05613-6.
Other Identifiers
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2017YFC0909301
Identifier Type: -
Identifier Source: org_study_id
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