Objective To evaluated the serum uric acid(SUA)level in elderly patients with type 2 diabetes mellitus(T2DM)and sarcopenia,and to explore the relationship between SUA and T2DM with sarcopenia.Methods A total of 522 hospitalized patients with T2DM who were admitted to the Department of Endocrinology,The First Affiliated Hospital of Xinjiang Medical University were enrolled in this study from January 2022 to September 2023.All the patients were divided into T2DM without sarcopenia group(n=431)and T2DM with sarcopenia group(Sar,n=91)according to the presence of sarcopenia.In addition,participants were divided into four groups:Q1BMI(≤23.70 kg/m2),Q2BMI(23.80～26.10 kg/m2),Q3BMI(26.20～29.00 kg/m2),and Q4BMI(>29.00 kg/m2)according to BMI quartiles.Besides,depending on SUA quartiles,they were divided intofour groups Q1SUA(≤269.00 μmol/L),Q2SUA(269.01～313.00 μmol/L),Q3SUA(313.01～363.00 μmol/L),and Q4SUA(>363.00 μmol/L).The clinical data and biochemical indexes were compared between groups.Pearson correlation analysis was used to investigate the correlation between ASMI and other indexes.The influencing factors for sarcopenia in elderly T2DM were analyzed by logistic regression.Results The age,HbA1c,and 2 hPG levels were higher,while the BMI,FC-P,SUA,ASMI,grip strength,and gait speed were lower in the Sar group than in the T2DM group(P<0.05).Pearson correlation analysis showed that SUA was positively correlated with ASMI(P<0.05).Logistic regression analysis revealed that SUA was still the influencing factor for sarcopenia after adjusting for age,BMI,and 2 hPG.The risk of sarcopenia was higher in the Q1BMI and Q2 BMI groups than in the Q4BMI group(P<0.05),and the risk of sarcopenia was higher in the Q1SUA group than in the Q4SUA group(P<0.05).Conclusion SUA is a protective factor in elderly T2DM patients with sarcopenia.Elderly T2DM patients with low SUA are prone to develop sarcopenia.

Objective:To establish and verify a diagnostic model for distinguishing multiple sclerosis (MS) from other neurological diseases with similar symptoms by usingcerebrospinal fluid oligoclonal band (CSF-OCB)combined with IgG intrathecal synthesis indicators and biochemical markers.Methods:Multiple sclerosis (MS) patients admitted to the Neurology Department of Beijing Tiantan Hospital affiliated with Capital Medical University from January 2014 to December 2022 were selected as the case group, while patients with similar neurological symptoms were selected as the control group. Using the case-control study design, a retrospective analysis was conducted on the detection of age, gender, oligoclonal bands in cerebrospinal fluid, IgG intrathecal synthesis indicators and biochemical indicators for all study subjects. The differential diagnosis model was determined by the multiple logistic regression analysis, and the receiver operating characteristic (ROC) curve was used to analyze the diagnostic efficiency of the differential diagnosis model for neurological diseases with similar symptoms to MS and other conditions.Results:This study included 167 patients in the case group and 335 patients in the control group, of which 128 patients in the case group and 265 patients in the control group were used to construct the model, and 39 patients in the case group and 70 patients in the control group were used for model validation. The differential diagnostic model constructed by a multivariate logistic regression model was Y=0.871×CSF-OCB-0.051×CSFprotein-0.231×CSFchloride+1.183×gender-0.036×LDH+35.770. The model showed that the area under the curve, sensitivity and specificity were respectively 0.916, 87.3% and 87.6%. The Delong test results showed that the diagnostic efficacy of the model was significantly different from OCB, IgG intrathecal synthesis indicators, and OCB combined with IgG intrathecal synthesis indicators ( P<0.05). The new model validation showed that the actual diagnostic consistency rate for the MS group was 84.6%, while the actual diagnostic consistency rate for the control group was 90.0%. Conclusion:This study combines OCB, IgG intrathecal synthesis indicators, and biochemical indicators to establish a diagnostic prediction model for neurological diseases with similar clinical symptoms in MS. This model may have good differential diagnostic value and can better assist clinical diagnosis in the early stages of disease progression in MS patients.

Objective:To establish and verify a diagnostic model for distinguishing multiple sclerosis (MS) from other neurological diseases with similar symptoms by usingcerebrospinal fluid oligoclonal band (CSF-OCB)combined with IgG intrathecal synthesis indicators and biochemical markers.Methods:Multiple sclerosis (MS) patients admitted to the Neurology Department of Beijing Tiantan Hospital affiliated with Capital Medical University from January 2014 to December 2022 were selected as the case group, while patients with similar neurological symptoms were selected as the control group. Using the case-control study design, a retrospective analysis was conducted on the detection of age, gender, oligoclonal bands in cerebrospinal fluid, IgG intrathecal synthesis indicators and biochemical indicators for all study subjects. The differential diagnosis model was determined by the multiple logistic regression analysis, and the receiver operating characteristic (ROC) curve was used to analyze the diagnostic efficiency of the differential diagnosis model for neurological diseases with similar symptoms to MS and other conditions.Results:This study included 167 patients in the case group and 335 patients in the control group, of which 128 patients in the case group and 265 patients in the control group were used to construct the model, and 39 patients in the case group and 70 patients in the control group were used for model validation. The differential diagnostic model constructed by a multivariate logistic regression model was Y=0.871×CSF-OCB-0.051×CSFprotein-0.231×CSFchloride+1.183×gender-0.036×LDH+35.770. The model showed that the area under the curve, sensitivity and specificity were respectively 0.916, 87.3% and 87.6%. The Delong test results showed that the diagnostic efficacy of the model was significantly different from OCB, IgG intrathecal synthesis indicators, and OCB combined with IgG intrathecal synthesis indicators ( P<0.05). The new model validation showed that the actual diagnostic consistency rate for the MS group was 84.6%, while the actual diagnostic consistency rate for the control group was 90.0%. Conclusion:This study combines OCB, IgG intrathecal synthesis indicators, and biochemical indicators to establish a diagnostic prediction model for neurological diseases with similar clinical symptoms in MS. This model may have good differential diagnostic value and can better assist clinical diagnosis in the early stages of disease progression in MS patients.