Clinical efficacy and safety evaluation of Dermatophagoides farinae drops in the treatment of allergic rhinitis with epistaxis.

Summary: Background. Epistaxis is frequently observed in allergic rhinitis (AR) patients. However, few studies focus on the outcome of epistaxis with treatment of AR patients. This study aimed to retrospectively analyze the efficacy and safety of AR patients with epistaxis treated with sublingual immunotherapy (SLIT). Methods. A total of 74 patients aged 4-60 years with house dust mite (HDM)-induced AR accompanied by epistaxis and who completed 1 year of SLIT treatment with standard Dermatophagoides farinae (D. farinae) drops were enrolled in this study. The symptom scores, total medication scores (TMS), combined symptom and medication score (CSMS), visual analog scales (VAS), and bleeding score (BS) were assessed, as well as the nasal endoscopic examinations were performed to observe nasal signs. Results. The levels of symptom scores, TMS, CSMS, VAS, and BS at 0.5 year and 1 year of SLIT treatment were significantly lower than those at the baseline (all p less than 0.01). Also, statistical differences were seen in CSMS (p less than 0.05) and VAS (p less than 0.01) between 0.5 year and 1 year. As expected, BS was positively correlated with CSMS (r = 0.617, 95% CI 0.517-0.699) and VAS (r = 0.777, 95% CI 0.719-0.822) at all three time points. Conclusions. SLIT with D. farinae drops was effective and safe for AR patients with epistaxis, resulting in improving the symptoms of rhinitis while relieving the symptoms of epistaxis.

[Construction of a diagnostic model for fatty liver using human body composition analysis].

Objective: To construct a diagnostic model for fatty liver using body composition analysis and further evaluate the diagnostic effect of the model on fatty liver. Methods: 726 cases with chronic liver disease who visited Tianjin Second People's Hospital from April 2019 to June 2022 and had body composition analysis tests were retrospectively enrolled and were divided into a fatty liver group (551 cases with fatty liver) and a control group (175 cases without fatty liver) according to the measured values of abdominal ultrasound and controlled attenuation parameter. An independent sample t-test and a non-parametric rank sum test were used for statistical processing. Logistic regression was used to construct a diagnostic model. Hosmer-Lemeshow was used to validate the fit of model. Receiver operating characteristic curve was used to confirm the diagnostic efficiency of the model. In addition, 341 cases of chronic liver disease who visited Tianjin Second People's Hospital were included to further verify the application effect of the model between July 2022 and February 2023. Results: Compared with the control group, the differences in various indicators of body composition analysis in the fatty liver group were statistically significant (P < 0.05). Basal metabolic rate (X1), visceral fat area (X2), and body fat (X3) were eventually included in the diagnostic model for BCA-FL (body composition analysis-fatty liver)= -7.771+0.002X1-0.035X2+0.456X3 with the Hosmer-Lemeshow test (P=0.059). The measured area under the receiver operating characteristic curve, the sensitivity, and the specificity were 0.888, 0.889, and 0.726, respectively, when the diagnostic threshold value was 0.615 with the Youden index and the receiver operating characteristic curve. In the validated model group, the area under the receiver operating characteristic curve, Youden index, sensitivity, and specificity were 0.875, 0.624, 0.799, and 0.825, respectively. Conclusion: The diagnostic model BCA-FL for fatty liver constructed using human body composition analysis has good diagnostic efficacy and is suitable for screening fatty liver in different basic liver disease populations.

[Risk prediction of metabolic syndrome and coronary artery disease in overweight and obese populations based on serum metabolomics].

Objective: By identifying different metabolites in the serum and clarifying the potential metabolic disorder pathways in metabolic syndrome (MS) and stable coronary artery disease patients, to evaluate the predictive value of specific metabolites based on serum metabolomics for the occurrence of MS and coronary heart disease in overweight or obese populations. Methods: This is a retrospective cross-sectional study. Patients with Metabolic Syndrome (MS group), patients with stable coronary heart disease (coronary heart disease group), and overweight or obese individuals (control group) recruited from the Central District of the First Affiliated Hospital of Zhengzhou University from 2017 to 2019 were assigned to the training set, meanwhile, the corresponding three groups of people recruited from the East District of the hospital during the same period were assigned to the validation test. The serum metabolomics profiles were determined by ultra-performance liquid chromatography-quadrupole/orbitrap high-resolution mass spectrometry (UHPLC-Q-Orbitrap HRMS). Clinical characteristics (age, gender, body mass index (BMI), blood pressure, fasting plasma glucose (FPG), glycosylated hemoglobin (HbA1c), alanine aminotransferase (ALT), aspartate transaminase (AST), total cholesterol (TC), triacylglycerol (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), glomerular filtration rate (eGFR), creatinine (CR)) were also collected. Based on the orthogonal partial least-squares discrimination analysis (OPLS-DA) model, the significantly changed metabolites for MS and coronary artery disease patients were screened according to variable important in projection (VIP), and the receiver operating characteristic (ROC) analysis was evaluated for the risk prediction values of changed metabolites. Results: A total of 488 subjects were recruited in this study, the training set included 40 MS, 249 coronary artery disease patients and 148 controls, the validation set included 16 MS, 18 coronary artery disease patients and 17 controls. We made comparisons of the serum metabolites of coronary artery disease vs. controls, MS vs. controls, and coronary artery disease vs. MS, and a total of 22 different metabolites were identified. The disturbed metabolic pathways involved were phospholipid metabolism, amino acid metabolism, purine metabolism and other pathways. Through cross-comparisons, we identified 2 specific metabolites for MS (phosphatidylcholine (18∶1(9Z)e/20) and pipecolic acid), 4 specific metabolites for coronary artery disease (lysophosphatidylcholine (17∶0), PC(16∶0/16∶0), hypoxanthine and histidine), and 4 common metabolites both for MS and coronary artery disease (isoleucine, phenylalanine, glutathione and LysoPC(14∶0)). Based on the cut-off values from ROC curve, the predictive value of the above metabolites for the occurrence of MS in overweight or obese populations is 100%, the predictive value for the occurrence of coronary heart disease is 87.5%, and the risk predictive value for coronary heart disease in MS patients is 82.1%. Conclusions: The altered serum metabolites suggest that MS and coronary heart disease may involve multiple metabolic pathway disorders. Specific metabolites based on serum metabolomics have good predictive value for the occurrence of MS and coronary heart disease in overweight or obese populations.