The Clinical Application Of Nontraditional Glycemic Markers(GA,FA,1,5-AG)

PART? Performance characteristics of enzymatic glycated albumin assay and effect of albumin detection on glycated albuminObjective To evaluate the methodology performance characteristics of enzymatic glycated albumin assay;to investigate the discrepancy between BCG and m BCP for serum albumin determination in the assay of glycated albumin value and the correlation of bromocresol green(BCG),bromocresol purple(BCP)and immunoturbidmetic assay(ITA)for the measurement of serum albumin.Methods The performance characteristics of the Lucica GA-L enzymatic assay(Asash kasei pharma,Tokyo,Japan): accuracy,imprecision,linearity range,recover study and reference interval.A total of 306 fresh human serum samples were randomly selected.Alb concentrations were measured by bromcresol green(BCG),modified bromcresol purple(m BCP),immunoturbidmetic assay(ITA).GA concentrations were detected by enzymatic method.GA value calculated by Alb and GA concentrations(GA value = 100*GA/Alb).Results The mean bias of GA was 2.75% calculated by within group mean.The repeatability and reproducibility CV of GA were less than 3%.The imprecision were lower than that declared by the producer.The results of GA in healthy check were fall in the reference range(11%~16%).In the evaluation of linearity range,the regression equation was Y=1.0153X+0.1608,R2=0.9991 and the linearity range in this study was 0.45~31.2 g/L,which satisfies the linear requirement.In recovery test,the average deviation between the control sample and the standard sample was 1.57g/L,and the average standard recovery was 102.46%.When Alb?40 g/L,BCG,m BCP and ITA had no significant clinical difference and there was no significant difference between GABCG and GAm BCP(p = 0.537);When Alb<40g/L,BCG had significant differences between m BCP and ITA(p < 0.01),GAITA(p > 0.05)and GABCG were significantly lower than GAm BCP(p < 0.01).Conclusion The enzymatic assay(Lucica GA-L kit)that is used to detect GA have a good analytical performance,which satisfies the requirement of clinical application.No obvious clinical significance of Alb concentrations was observed measured by BCG,m BCP and ITA when Alb ? 40g/L.And different Alb methods for GA calculation had no difference.When Alb < 40g/L,the consistency of m BCP and ITA was better than that of BCG and ITA,which indicated m BCP method may be superior for the assay of GA value,whereas the GA data may be underestimated if using BCG method for the determination of Alb.PART ? The relationship between glycemic markers by linear regression,non-linear fitting and the diagnostic performance of glycemic markers for diagnosis of diabetesObjective We compared linear regression with non-linear fitting to assess the relationship between glycemic markers and the diagnostic performance of glycemic markers for the diagnosis of diabetes.Methods In this retrospective study,a total of 549 patients were enrolled,whose Hb A1 c,fasting glucose(FPG),GA,and fructosamine(FA)were simultaneously measured.In non-linear model,coefficient of determination was used to identify the best model.The difference between correlation coefficient of linear and non-linear model was conducted by Fisher Z-transform.The area under the curve(AUC)of glycemic markers for identifying diabetes was determined by receiver-operating characteristic(ROC)curve analysis.Results GA and FA were significantly correlated with Hb A1 c and FPG,whereas GA was more strongly correlated with either Hb A1 c compared with FA in both linear and non-linear model(both p < 0.001).Overall,compared with linear regression,best non-linear model improved the correlation from 0.828 to 0.871(p < 0.05)for GA and Hb A1 c and from 0.618 to 0.792(p < 0.01)for GA and FPG.In diabetes group,linear and non-linear model performed comparably for GA,FA and Hb A1 c,FPG.But non-linear model better describe the relationship between glycemic markers than linear regression in non-diabetic patients.When diabetes was defined by history diabetes,Hb A1 c achieved the highest AUC of 0.831 [95% confidence interval(CI),0.796~0.867] than GA(AUC=0.811;95% CI,0.774~0.849)and FA(AUC=0.758;95% CI,0.717~0.798),while only 0.695(95% CI,0.651~0.740)for FPG.Conclusion Non-linear fitting models may suitable to describe the relationship between GA,FA and Hb A1 c,FPG.GA and FA can be worthful supplement to Hb A1 c and FPG,even suggest a potential utility for the diagnosis of diabetes.PART ? The establishment of biological reference intervals of nontraditional glycemic markers based on local populationObjective We established the reference intervals for glycated albumin(GA),fructosamine(FA),and 1,5-anhydroglucitol(1,5-AG)in a Chinese healthy population.Methods This study enrolled a total of 458 eligible reference individuals,consisted of 226 men and 232 women,aged from 20~79 years(median age 43 years),who attending routine healthy checks.We stratified the subjects according to gender(males and females)and age(20~39 years,40~59 years,and 60~79 years),and combined statistical methods with Lahti algorithm,as well as appropriate clinical consideration,to judge whether partitioning for data was needed.Results GA levels between males and females were statistically different(p < 0.001),but the absolute difference between the upper reference limits only 0.31%,which was too small to be clinically relevant.GA levels across the three age groups were statistically different(p < 0.001),Lahti algorithm suggested partitioning for 20~59 years and 60~79 years,which reference intervals were 10.38~13.89%,10.23-14.79%,respectively.1,5-AG levels in males were significant higher than females(p < 0.001),absolute difference was 51 ?mol/L(8.5 ?g/m L)in mean level.Thus,partitioning for gender was needed.Reference intervals for 1,5-AG were 107~367 ?mol/L for males and 79~306 ?mol/L for females.The absolute difference of the lower reference limits for FA only 7 ?mol/L between males and females.FA levels across the three age groups were not statistically different(p > 0.05).The reference interval for FA was 220~298 ?mol/L.Conclusion New reference intervals for nontraditional glycemic markers were established based on a Chinese population.