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Objective:To examine the associations between serum magnesium (Mg) concentration and metabolic syndrome (MetS), diabetes (DM), hypertension (HTN), hyperuricemia (HU) in rheumatoid arthritis (RA) patients.Methods:From June 29, 2013 to February 29, 2019, 1 009 RA patients were collected at Tianjin First Central Hospital. The Mg concentration, clinical and laboratory indicators, living habits and medication were collected. The multivariate adjusted Logistic regression model was used to evaluate the relationship between Mg and MetS, DM, HTN, and HU.Results:Compared with the lowest quintile serum magnesium, In the MetS regression analysis, OR and 95% CI were 0.26 [95% CI(0.09, 0.76), P=0.01)], 0.17 [95% CI (0.06, 0.47), P<0.01], 0.27 [95% CI(0.09, 0.76), CI=0.01], and 0.22 [95% CI(0.09, 0.58), P<0.01], in second, third, fourth, and fifth (highest) quantiles of serum Mg, respectively, P for trend <0.01. In DM regression analysis, after adjusting for multiple potential confounders in Model 2, OR and 95% CI were 0.58 [95% CI (0.35, 0.96), P=0.03], and 0.51 [95% CI (0.29, 0.89), P=0.02], in third and fifth quantiles of the serum Mg, respectively, P for trend =0.01. But in model 3 this correlation disappears. HTN and HU were not associated with serum Mg ( P>0.05), P for trend >0.05. Conclusion:There was a negative correlation between serum Mg concentration and MetS in RA patients, But no correlation with HTN, HU. After adjusting the glomerular filtration rate, the negative correlation between serum Mg and DM also disappeared.
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Objective:To explore the difference between the prevalence of metabolic syndrome (MetS) and its influencing factors between rheumatoid arthritis (RA) patients and osteoarthritis (OA) patients.Methods:2 024 patients with RA were selected as the experimental group inthe First Central Hospital of Tianjin from January 1, 2014 to March 12, 2019. A total of 1 271 patients with OA who were treated at the same time served as the control group.Relevant indicators were measured, and correlation analysis was performed using logistic regression.Results:The prevalence of MetS in the past six years did not differ significantly inpatients with RA ( χ2=5.77, P=0.06) and OA ( χ2=1.11, P=0.57). MetS risk factors were more prevalent inpatients with OA, and the prevalence of MetS in patients with OA was also higher than patients with RA ( χ2=19.07, P<0.01). In logistic regression model, the prevalence of MetS was positively correlated with body weight {RA: 1.14[(95% CI(1.10, 1.18), P<0.01]; OA: 1.11[95% CI(1.07, 1.16), P<0.01]}, blood levels of triglyceride {RA:2.64[95% CI(1.58, 4.40), P<0.01] and OA: 2.91[95% CI(1.32, 6.43), P=0.01]}, and glucose {RA:1.67[95% CI(1.42, 1.97), P<0.01] and OA: 1.76[95% CI(1.31, 2.35), P<0.01]}, and negatively correlated with blood level of high-density lipoprotein cholesterol{RA: 0.09[95% CI(0.02, 0.32), P<0.01] and OA: 0.03[95% CI(0.01, 0.25), P<0.01]} in patients with RA and OA. MetS risk factors such as serum creatinine (Scr) only presented in RA patients 1.04 [ OR: 95% CI(1.01, 1.06), P<0.01]. Conclusion:The prevalence of MetS did not decrease in patients with chronic inflammatory arthritis in the past six years, and OA patients havea greater risk of MetS.
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BACKGROUND:The physical properties of macromolecule ocular prosthesis materials, such as density, hardness, mechanical strength, can affect the working accuracy of ocular prosthesis and can also affect the surface roughness of ocular prosthesis, thus affecting the comfort when the patients wear. OBJECTIVE:To study the differences of different macromolecule ocular prosthesis materials in bubble generation rate, hardness and surface roughness. METHODS:Thirty-seven test pieces, 30 mm×30 mm×2 mm, were made and then divided to seven groups according to their brands, caled groups A, B, C, D, E, F, G, each group of five pieces. One surface of each test piece was polished in accordance with the principle of coarse to fine, the surface roughness of the two surfaces and hardness on the polished surface were measured, and then the bubble formation rate of the material was calculated. RESULTS AND CONCLUSION:The surface roughness values of groups A-G were (0.078±0.016), (0.074±0.019), (0.075±0.022), (0.066±0.020), (0.075±0.017), (0.068±0.015), and (0.067±0.017) μm, respectively. There was no significant difference in the surface roughness between the groups (P > 0.05). The hardness values of groups A-G were 766.92±3.71, 771.84±14.51, 791.20±9.64, 804.50±4.49, 779.00±17.92, 772.20±19.18, 704.00±7.23, respectively. There was a significant difference in the hardness between groups D, G and the other groups (P 0.05). The bubble generation rates of groups A-G were (8.87±0.29)%, (8.29±1.02)%, (6.94±0.43)%, (4.83±0.20)%, (7.59±0.19)%, (8.61±0.25)%, (4.89±0.17)%, respectively. There was a significant difference in the bubble generation rate between groups D, G and the other groups (P 0.05), as wel as between the other groups (P > 0.05). To a certain extent, the smaler the bubble formation rate is, the greater the hardness and the smaler the surface roughness are.