| Background:Diabetic blood glucose disorders are mainly due to abnormalities of glycemic central trends and variability.Glycated hemoglobin is the gold standard for measuring blood glucose control level,but it has many limitations in reflecting actual blood glucose changes.Continuous glucose monitoring(CGM)records subjects’detailed glucose profile.Many studies have proposed different parameters for measuring blood glucose control based on CGM data.However,most of them focus on the quantification of single abnormal features of blood glucose,and there is no ideal index to comprehensively evaluate multiple characteristics of blood glucose.This study proposed a new method to comprehensively quantify the deviation of blood glucose from normal level.Objective:1.Based on previous studies,a new method for calculating glycemic control parameters was proposed,so that it can comprehensively quantify blood glucose abnormalities including hyperglycemia,hypoglycemia and glycemic variability,and named as glycemic deviation index(GDI).2.To analyze the ability of GDI to evaluate blood glucose control level in simulated blood glucose data and CGM data of type 2 diabetes patients,compare GDI with similar blood glucose parameters,and discuss the clinical application value of GDI.Methods:1.The dynamic blood glucose data with other clinical data collection:Study participants were T2DM inpatients from the department of endocrinology at Shandong Provincial Hospital affiliated with Shandong University enrolled from October 2017 to May 2018.A total of 30 cases were included.Those patients with secondary diabetes,acute infection,stress conditions,severe organic lesions,acute diabetic complications or pregnant diabetic women and those with insufficient data were excluded.Their iPro2 CGM data and laboratory tests results were collected.2.Calculation of blood glucose parameters:The blood glucose parameters mentioned in this paper were obtained by programing in Python and iPro2 analysis software CareLink Plus 1.0.3.Formula design and derivation:MATLAB 7.0 is used for formula derivation.4.Statistical analysis:SPSS 23.0 was used for one-way ANOVA,factor analysis,correlation analysis,ROC curve analysis and other statistical calculations.Results:1.Both the 24h simulated blood glucose data and the real patient data showed that the presence of hypoglycemia was not covered by the occurrence of hyperglycemia on the same day,and the deviation between blood glucose variability and mean blood glucose was simultaneously evaluated as an independent factor.The results of one-way ANOVA showed that there were statistically significant differences in clinical glycemic metrics,mean glycemic metrics and glycemic variability metrics among groups according to the severity of GDI.2.Two main factors were obtained through factor analysis,and the correlation between GDI and glycerite variability was superior compared with M-value and J-index.ROC curve analysis showed that GDI had higher diagnostic efficacy for glycemic disorder than M value and J index.Summary and conclusions:The new glycemic parameter GDI is a monitoring index that integrates multiple blood glucose measures.GDI quantifies hyperglycemia,hypoglycemia,and glycemic variability based on CGM data,and improved diagnostic efficiency of glycemic deviation.Therefore,the GDI may contribute to the screening and evaluation of glycemic disorders,and be a supplement to current clinical glycemic parameters according to distinguished monitoring time.Further studies with larger databases are needed to validate this novel index.Background:Uric acid is the end product of purine metabolism.The incidence of hyperuricemia is affected by regional dietary habits and lifestyle,and has become the second most common metabolic disease after diabetes in China.Serum uric acid(SUA)elevation is not only the basis of gout,but also an independent risk factor for endocrine and metabolic diseases such as diabetes and hyperlipidemia.The thyroid gland is an important endocrine organ which regulates the metabolism and growth of the body.Abnormal thyroid function can be caused by a variety of reasons,and is often accompanied by disorders of other endocrine and metabolic processes,among which the research conclusion on the relationship between increased SUA and thyroid function is somewhat contradictory and the mechanism is still unclear.Different studies have shown that hyperthyroidism,hypothyroidism and subclinical thyroid dysfunction are associated with increased incidence of hyperuricemia.Objective:1.To analyze the relationship between SUA and thyroid function related indicators,hyperuricemia and thyroid dysfunction in the physical examination population in Jinan of Shandong province.2.To explore the potential relationship between elevated SUA and thyroid function,and to provide evidence for the interaction between the two in clinical screening.Methods:1.Health-screening data of the physical examination center of Shandong provincial hospital in 2015 were collected,excluding incomplete data,patients with a history of drug use affecting SUA and thyroid function,and patients with a history of renal disease and other thyroid diseases,a total of 4248 cases were included.2.According to the diagnostic criteria of hyperuricemia,the population was divided into hyperuricemia group and non-hyperuricemia group,and t test was used to analyze and compare the influence of hyperuricemia on thyroid function and other clinical indicators in different genders.3.Using Pearson correlation analysis of thyroid stimulating hormone,free thyroxine,free triiodothyronine and the correlation of SUA,through multiple linear regression,respectively after adjustment for influencing factors,further analysis of FT3 and SUA relations.4.Chi-square test was used to analyze the incidence of 4 types of thyroid dysfunction in hyperuricemia and non-hyperuricemia males and females as well as the overall composition ratio of thyroid function status.Results:1.There were no statistically significant differences in TSH,FT4 and FT3 between the hyperuricemia group and the non-hyperuricemia group(P=0.567,0.215,0.918).2.Pearson correlation analysis showed that TSH,FT4,FT3 and SUA were weakly correlated(r=-0.07,0.15,0.21).The FT3 model with the strongest correlation was put into the multiple linear regression model,and the contribution of FT3 to SUA was not statistically significant,nor was the multiple linear regression model with SUA into FT3.3.Chi-square test showed that there was no statistically significant difference in the incidence of the 4 kinds of abnormal thyroid function between hyperuricemia and non-hyperuricemia of different genders,nor was there statistically significant difference in the overall composition ratio of thyroid function status.Summary and conclusions:The relationships between TSH,FT4,FT3 and SUA are very weak and,among multiple factors,cannot account for hyperuricemia.There is no difference in the prevalence of certain thyroid dysfunction,as well as the distribution of thyroid function conditions in both males and females with various SUA levels in the total population. |
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