Objective Type 2 diabetes mellitus(T2DM) is closely related to sarcopenic obesity(SO). Body composition measurement including body weight, body mass index, waist circumference, percentage body fat, fat mass, muscle mass, visceral adipose tissue and subcutaneus adipose tissue, plays a key role in evaluating T2DM and SO. The weight reduction effect of sodium-glucose cotransporter 2(SGLT-2) inhibitors has been demonstrated. However, there are warnings that SGLT-2 inhibitors should be used with caution because they may increase the risk of sarcopenia. The effect of SGLT-2 inhibitors on body composition in T2DM is inconclusive. In this work, a meta-analysis of randomized controlled trials was conducted to evaluate the effect of SGLT-2 inhibitors on body composition in T2DM. Methods PubMed, the Cochrane Library, EMbase and Web of Science databases were searched by computer. All statistical analyses were carried out with Review Manager version 5. 3. Results were compared by weight mean difference(WMD), with 95% confidence intervals(CI) for continuous outcomes. A random effects model was applied regardless of heterogeneity. The I2 statistic was applied to evaluate the heterogeneity of studies. Publication bias was assessed using Funnel plots. Results 18 studies with 1430 participants were eligible for the meta-analysis. SGLT-2 inhibitors significantly reduced body weight(WMD:-2. 73kg, 95%CI: -3. 32 to -2. 13, p<0. 00001), body mass index(WMD:-1. 13kg/m2, 95%CI: -1. 77 to -0. 50, p = 0. 0005), waist circumference(WMD:-2. 20cm, 95%CI: -3. 81 to -0. 58, p = 0. 008), visceral fat area(MD:-14. 79cm2, 95%CI: -24. 65 to -4. 93, p = 0. 003), subcutaneous fat area(WMD:-23. 27cm2, 95% CI:-46. 44 to -0. 11, P = 0. 05), fat mass(WMD:-1. 16kg, 95%CI: -2. 01 to -0. 31, p = 0. 008), percentage body fat(WMD:-1. 50%, 95%CI:-2. 12 to -0. 87, P<0. 00001), lean mass(WMD:-0. 76kg, 95%CI:-1. 53 to 0. 01, P = 0. 05) and skeletal muscle mass(WMD:-1. 01kg, 95%CI:-1. 91 to -0. 11, P = 0. 03). Conclusion SGLT-2 inhibitors improve body composition in T2DM including body weight, body mass index, waist circumference, visceral fat area, subcutaneous fat area, percentage body fat and fat mass reduction, but cause adverse effects of reducing muscle mass. Therefore, until more evidence is obtained to support that SGLT-2 inhibitors increase the risk of sarcopenia, not only the benefit on body composition, but also the adverse effect of the reduction in muscle mass by SGLT-2 inhibitors in T2DM should be considered.

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G o u t i s a c h r o n i c r h e u m a t i c d i s e a s e c a u s e d b y hyperuricemia, resulting from the increase of purine biosynthesis or the disorder of uric acid excretion and causing the deposition of urate crystals synovium, bursa, cartilage, and other tissues. The diagnosis and treatment of complex and refractory gout is a major clinical problem to be solved. In the acute outbreak of gouty arthritis as a local acute inflammatory process, tumor necrosis factor-α (TNF-α), interleukin-1 (IL-1), and other cytokines play an essential role in the occurrence, development, and persistence of local inflammation in gout acute stage. Etanercept for injection is a recombinant human tumor necrosis factor receptor antibody fusion protein. One refractory gout was treated with TNF antagonists etanercept combined with febuxostat in our hospital, and satisfactory results were obtained. We present the following article in accordance with the CARE reporting checklist (available at http://dx.doi.org/10.21037/apm-20-2072). Case presentationThe patient was a 48-year-old male. He was admitted to the hospital on March 15, 2019, due to intermittent multiple joint swelling and pain for 17 years and aggravation for one month. He had hypertension and hyperlipidemia two years before this admission. Seventeen years ago, the patient developed pain of the first metatarsophalangeal joint of the right foot after drinking alcohol, accompanied by redness, swelling, and increased skin temperature. The disease was relieved one week after pain relief and symptomatic

BackgroundPrediabetes is an intermediate metabolic state between euglycaemia and diabetes, including three different definitions: impaired fasting glucose, impaired glucose tolerance, and mildly elevated glycated haemoglobin (HbA1c) (range 5.7%–6.4%). The effect of prediabetes on bone mineral density (BMD) has not been established. Therefore, we performed a meta‐analysis to evaluate the association between prediabetes and BMD.MethodsWe retrieved studies related to prediabetes and BMD from PubMed, Web of Science, and Embase databases from January 1990 to December 2022. All data were analysed using the random effects model. Statistical heterogeneity was tested by I2. Subgroup analysis was performed after each study‐level variable was pre‐defined by meta‐regression.ResultsA total of 17 studies were included involving 45,788 patients. We detected a significant overall association of prediabetes with increased spine BMD (weighted mean difference [WMD] = 0.01, 95% CI [0.00, 0.02], p = 0.005; I2 = 62%), femur neck (FN) BMD (WMD = 0.01, 95% CI [0.00, 0.01], p < 0.001; I2 = 19%), and femur total (FT) BMD (WMD = 0.02, 95% CI [0.01, 0.03], p < 0.001; I2 = 51%). Several variables leading to heterogeneity were defined by meta‐regression, including age, sex, region, study type, dual‐energy X‐ray absorptiometry scanner manufacturer, and prediabetes definition. Subgroup analyses indicated that the association of prediabetes with increased BMD was stronger in men, Asians, and older adults over 60 years of age.ConclusionsCurrent evidence shows that prediabetes is strongly associated with increased BMD of the spine, FN, and FT. The association was stronger among males, Asians, and older adults over 60 years of age.

Background:: Type 1 diabetes is a chronic autoimmune disease featured by insulin deprivation caused by pancreatic β-cell loss, followed by hyperglycaemia. Objective: Currently, there is no cure for this disease in clinical treatment, and patients have to accept a lifelong injection of insulin. The exploration of potential diagnosis biomarkers through analysis of mass data by bioinformatic tools and machine learning is important for Type 1 diabetes. Methods: We collected two mRNA expression datasets of Type 1 diabetes peripheral blood samples from GEO, screened out differentially expressed genes (DEGs) by R software, conducted GO and KEGG pathway enrichment using the DEGs. And the STRING database and Cytoscape were used to build PPI network and predict hub genes. We constructed a Logistic regression model by using the hub genes to assess sample type. Results: Bioinformatic analysis of GEO dataset revealed 92 and 75 DEGs in GSE50098 and GSE9006 datasets, separately, and 10 overlapping DEGs. PPI network of these 10 DEGs showed 7 hub genes, namely EGR1, LTF, CXCL1, TNFAIP6, PGLYRP1, CHI3L1 and CAMP. We built a Logistic regression basing on these hub genes and optimized the model to 3 genes (LTF, CAMP and PGLYRP1) based Logistic model. The values of area under curve (AUC) of training set GSE50098 and testing set GSE9006 were 0.8452 and 0.8083, indicating the efficacy of this model. Conclusion: Integrated bioinformatic analysis of gene expression in Type 1 diabetes and the effective Logistic regression model built in our study may provide promising diagnostic methods for Type 1 diabetes.