Biomarkers for coronary artery disease
A technology of coronary artery disease and biomarkers, applied in the field of biomarkers for coronary artery disease, can solve the confusion of the pathogenesis and metabolic system of patients with coronary artery disease, etc.
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Embodiment 1
[0061] Example 1. Identification of Biomarkers for Assessing Coronary Artery Disease Risk
[0062] 1.1 Sample Collection
[0063] Fecal samples from 165 southern Chinese subjects including 88 atherosclerotic cardiovascular disease (ACVD) patients and 77 control subjects (training set, Table 1) were collected by Guangdong Provincial People's Hospital in 2011 . Patients with ACVD were diagnosed and classified according to pathological features (coronary angiography). Subjects were asked to collect fresh stool samples at the hospital. Collected samples were placed in sterile tubes and immediately stored at -80 °C until further analysis.
[0064] Full ethical approval has been obtained, and all patients provided written informed consent. The study was approved by the Ethics Committee of Guangdong Provincial People's Hospital.
[0065] Table 1. Baseline characteristics of atherosclerotic cardiovascular disease (ACVD) cases and controls. The fourth column reports the results f...
Embodiment 2
[0136] Example 2. Validation of biomarkers in an additional 86 individuals
[0137] In order to verify the discriminative ability of the biomarkers (i.e. 65 selected MLGs from Streptococcus and 4 microorganisms), the inventors used another new independent research group, including 29 case samples and 57 control samples (Table 10) were also collected from Guangdong Provincial People's Hospital.
[0138] Table 10. Sample Information
[0139] Group cases control total test set 29 57 86
[0140] For each sample, DNA was extracted and a DNA library was constructed, followed by high-throughput sequencing as described in Example 1. The inventors estimated the relative abundance of the MLG in all samples by using the relative abundance values of genes from that MLG (Qin et al., 2012, supra).
[0141]Regarding the random forest model, the "randomForest 4.5-36" package was used in the R software version 2.10, and the input was the training data set (th...
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