Objective To investigate the mechanism of action and potential biomarkers of Jiangzhi Qingshen capsules in the treatment of dyslipidaemia based on clinical lipid metabolomics.Methods 30 patients with dyslipidaemia from Zhengzhou Hospital of Chinese Medicine were collected as the test group,and their lipid levels before and after taking Jiang Zhi Qing Shen capsule for 12 weeks were compared.Another 30 healthy patients were enrolled in the physical examination department of Zhengzhou Hospital of Chinese Medicine,and metabolomics investigation was carried out on plasma samples of the test group before and after the treatment as well as those of the healthy patients by LC-MS/MS technology.The differences between the groups were compared by multivariate statistical analysis,and the potential biomarkers were identified by HMDB and lipidblast databases,so as to clarify the possible pathways and targets for the treatment of mild dyslipidaemia by Jiangzhi Qingshen capsules.Results Jiangzhi Qingshen capsules could improve patients' blood lipid level,BMI and abdominal circumference significantly(P<0.05).And metabolomics results showed that 29 lipid metabolites in the plasma of the treated patients were dialled back,which involved cholesterol metabolism,fat digestion and absorption,glycerol-phospholipid metabolism and other biological processes.Conclusion The efficacy of Jiangzhi Qingshen capsules in treating dyslipidaemia was confirmed,and its mechanism of action might be related to the regulation of lipid metabolism.Metabolites such as acylcarnitine(Acar),phosphatidylethanolamine(PE)and phosphatidylcholine(PC)were expected to be used as the biomarkers of dyslipidaemia,so as to provide objective scientific basis for the lipid-lowering capsule,and to facilitate the promotion of its application.

Objective:To analyze the influencing factors of mood state of common rheumatic (rheumatoid arthritis; systemic lupus erythematosus; ankylosing spondylitis) patients and find out the common characteristics of patients with negative emotions, so as to identify and treat rheumatic patients with anxiety and depression in clinical practice.Methods:A total of 205 patients with rheumatism (83 with rheumatoid arthritis, 74 with systemic lupus erythematosus, 48 with ankylosing spondylitis) admitted to the Shandong Provincial Hospital Affiliated to Shandong University from April to May 2023 were included. The general condition and POMS of patients were collected. All patients were divided into 3 groups of low-TMD/ middle-TMD/ high-TMD(TMD≤90 scores; 90 scores105 scores). The χ2 test was used for categorical variables, and the continuous variables were tested for normal distribution. If they followed a normal distribution, the independent sample t test (for homogeneous variance) or the Welch′s t test (for unequal variance) was used for comparison between the two groups, and one-way analysis of variance was used for comparison between multiple groups; if they did not follow a normal distribution, the Kruskal-Wallis H test was used for comparison between groups. Patients with TMD>90 scores were defined as the negative emotion group. Gender, age, education level, economic income status, and TMD were included in the two-step cluster analysis (TCA) to obtain three cluster groups and analyze the differences between the groups. Logistic regression was used to analyze the risk factors. Results:The study found that patients with rheumatic diseases with low serum albumin levels ( F=6.26, P=0.003), low education level ( χ2=8.36, P=0.015), low economic level ( χ2=15.59, P<0.001), short disease course ( H=28.01, P<0.001), and high disease activity ( χ2=56.93, P<0.001) had higher TMD scores, which was not related to the type of disease ( χ2=4.81, P=0.090). The negative mood group had a higher proportion of females (57.6% vs. 73.4%, χ2=5.16, P=0.023), a shorter course of disease [24(12, 48) months vs. 48(24, 93) months, Z=13.58, P<0.001], and a lower serum albumin [(42.2±4.0) g/L vs. (44.0±3.4)g/L, t=-14.09, P=0.002], low level of education ( χ2=9.04, P=0.029), low level of income ( χ2=10.29, P=0.036), high disease activity ( χ2=61.91, P<0.001). The results of cluster analysis indicated that the above factors were significantly different among the three cluster groups except the course of disease. It is noteworthy that age ( F=19.25, P<0.001) and serum albumin ( F=5.64, P=0.004) had the lowest value and the highest TMD score ( F=5.64, P<0.001). Regression analysis showed that gender, disease course and disease activity were related factors to the occurrence of negative emotions, and high disease activity was a risk factor [ OR(95% CI) =26.58(4.53, 156.09), P<0.001]. Men [ OR(95% CI)=0.20 (0.08, 0.50), P=0.001) and the course of [ OR(95% CI)=0.99 (0.98, 1.00), P=0.007) as the protective factors. Conclusion:Male, serum albumin level, education level, income level, disease course and disease activity were the main factors affecting the mood state of patients with rheumatism, in which high disease activity was the risk factor, male and long disease course were the protective factors.

Objective To investigate the mechanism of action and potential biomarkers of Jiangzhi Qingshen capsules in the treatment of dyslipidaemia based on clinical lipid metabolomics.Methods 30 patients with dyslipidaemia from Zhengzhou Hospital of Chinese Medicine were collected as the test group,and their lipid levels before and after taking Jiang Zhi Qing Shen capsule for 12 weeks were compared.Another 30 healthy patients were enrolled in the physical examination department of Zhengzhou Hospital of Chinese Medicine,and metabolomics investigation was carried out on plasma samples of the test group before and after the treatment as well as those of the healthy patients by LC-MS/MS technology.The differences between the groups were compared by multivariate statistical analysis,and the potential biomarkers were identified by HMDB and lipidblast databases,so as to clarify the possible pathways and targets for the treatment of mild dyslipidaemia by Jiangzhi Qingshen capsules.Results Jiangzhi Qingshen capsules could improve patients' blood lipid level,BMI and abdominal circumference significantly(P<0.05).And metabolomics results showed that 29 lipid metabolites in the plasma of the treated patients were dialled back,which involved cholesterol metabolism,fat digestion and absorption,glycerol-phospholipid metabolism and other biological processes.Conclusion The efficacy of Jiangzhi Qingshen capsules in treating dyslipidaemia was confirmed,and its mechanism of action might be related to the regulation of lipid metabolism.Metabolites such as acylcarnitine(Acar),phosphatidylethanolamine(PE)and phosphatidylcholine(PC)were expected to be used as the biomarkers of dyslipidaemia,so as to provide objective scientific basis for the lipid-lowering capsule,and to facilitate the promotion of its application.

Objective:To analyze the influencing factors of mood state of common rheumatic (rheumatoid arthritis; systemic lupus erythematosus; ankylosing spondylitis) patients and find out the common characteristics of patients with negative emotions, so as to identify and treat rheumatic patients with anxiety and depression in clinical practice.Methods:A total of 205 patients with rheumatism (83 with rheumatoid arthritis, 74 with systemic lupus erythematosus, 48 with ankylosing spondylitis) admitted to the Shandong Provincial Hospital Affiliated to Shandong University from April to May 2023 were included. The general condition and POMS of patients were collected. All patients were divided into 3 groups of low-TMD/ middle-TMD/ high-TMD(TMD≤90 scores; 90 scores105 scores). The χ2 test was used for categorical variables, and the continuous variables were tested for normal distribution. If they followed a normal distribution, the independent sample t test (for homogeneous variance) or the Welch′s t test (for unequal variance) was used for comparison between the two groups, and one-way analysis of variance was used for comparison between multiple groups; if they did not follow a normal distribution, the Kruskal-Wallis H test was used for comparison between groups. Patients with TMD>90 scores were defined as the negative emotion group. Gender, age, education level, economic income status, and TMD were included in the two-step cluster analysis (TCA) to obtain three cluster groups and analyze the differences between the groups. Logistic regression was used to analyze the risk factors. Results:The study found that patients with rheumatic diseases with low serum albumin levels ( F=6.26, P=0.003), low education level ( χ2=8.36, P=0.015), low economic level ( χ2=15.59, P<0.001), short disease course ( H=28.01, P<0.001), and high disease activity ( χ2=56.93, P<0.001) had higher TMD scores, which was not related to the type of disease ( χ2=4.81, P=0.090). The negative mood group had a higher proportion of females (57.6% vs. 73.4%, χ2=5.16, P=0.023), a shorter course of disease [24(12, 48) months vs. 48(24, 93) months, Z=13.58, P<0.001], and a lower serum albumin [(42.2±4.0) g/L vs. (44.0±3.4)g/L, t=-14.09, P=0.002], low level of education ( χ2=9.04, P=0.029), low level of income ( χ2=10.29, P=0.036), high disease activity ( χ2=61.91, P<0.001). The results of cluster analysis indicated that the above factors were significantly different among the three cluster groups except the course of disease. It is noteworthy that age ( F=19.25, P<0.001) and serum albumin ( F=5.64, P=0.004) had the lowest value and the highest TMD score ( F=5.64, P<0.001). Regression analysis showed that gender, disease course and disease activity were related factors to the occurrence of negative emotions, and high disease activity was a risk factor [ OR(95% CI) =26.58(4.53, 156.09), P<0.001]. Men [ OR(95% CI)=0.20 (0.08, 0.50), P=0.001) and the course of [ OR(95% CI)=0.99 (0.98, 1.00), P=0.007) as the protective factors. Conclusion:Male, serum albumin level, education level, income level, disease course and disease activity were the main factors affecting the mood state of patients with rheumatism, in which high disease activity was the risk factor, male and long disease course were the protective factors.