Application of exosomal metabolites as markers of bipolar disorder
A bipolar disorder, metabolite technology, applied in the fields of biology and medicine, can solve problems such as differences in diagnosis results
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Embodiment 1
[0117] The subjects were 32 patients with bipolar disorder admitted to Foshan Third People's Hospital, and 40 age- and gender-matched healthy volunteers were recruited as controls. In addition, 31 patients with depression and 42 patients with schizophrenia were included. All physicians participating in the diagnosis work have the qualifications of practicing psychiatrists and have more than 10 years of experience in psychiatry. They are proficient in using the SCID-1 checklist, proficient in the ICD-10 and DSM-V diagnostic criteria, and use the Hamilton Depression Scale ( HAMD) and the Mania Scale (BRMS) were used to assess the psychopathological status of patients with bipolar disorder; the PNASS scale was used to assess the psychopathic status of patients with schizophrenia; the Hamilton Depression Rating Scale (HAMD) and Montgomery The Depression Scale (MADRS) was used to evaluate the psychopathological state of depressed patients. The operation was standardized and the con...
Embodiment 2
[0127] After extracting exosomes from the sample in Example 1, the samples were analyzed for metabolites in whole exosomes, and the expression abundance was statistically analyzed.
[0128] 1. Screening of significantly different exosome metabolites
[0129] In this embodiment, there are expression levels of 143 samples, including 40 healthy controls, 32 patients with bipolar disorder, 31 patients with depression and 40 patients with schizophrenia. In this example, a total of 350 exosome metabolites were included in the differential analysis.
[0130] In this example, OPLS-DA was used for differential analysis, and a p value > 1.0 was defined as differential metabolites, and finally 38 differential metabolites were screened (Table 2). figure 2 The S-plot diagram of the OPLS-DA analysis is shown, where the abscissa represents the co-correlation coefficient between the principal component and the metabolite, and the ordinate represents the correlation coefficient between the p...
Embodiment 3
[0134] Random forest algorithm model training was performed on 16 control samples and 12 bipolar disorder samples in the training set. In the present invention, the exosome metabolic markers were further analyzed and screened from the differentially expressed exosome metabolites in the first batch of data, and passed After various analysis and research, 15 differential metabolites were finally screened (Table 3).
[0135] Table 3. Exosome metabolic markers screened from the training set
[0136]
[0137] Analysis results:
[0138] 1. Screening for exosome metabolic markers
[0139] See Table 2 for the expression levels of the 15 exosome metabolic markers screened out in the present invention in the training set. Its ROC curve is as Figure 3A shown. The AUC index of 15 exosome metabolic markers in the training set samples was 83.8%.
[0140] 2. Validate the screened exosome metabolic markers
[0141] The diagnostic accuracy of the 15 exosome metabolic markers screened...
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