Study On The Changes Of Trace Elements And Clinical Prediction Model In Type 1 Diabetes And Diabetes Ketoacidosis Patients

Objective:Type 1 diabetes(T1D)exhibited sex-specific metabolic status including oxidative stress with dynamic change of trace elements,which emphasized the importance of the evaluation of trace elements according to sex.Besides,the insulin auto-antibodies could not be found in every T1D patient.If some high-risk groups of T1D could predict and prevent the occurrence of disease through common clinical parameters,it would benefit the early detection and treatment.In addition,diabetes ketoacidosis(DKA)was a complication of diabetes characterized by severe oxidative stress,with an acute onset and a high mortality rate.Hence,there was an urgent need to construct more effective and scientifically statistical prediction models to serve clinic better.This study aimed to evaluate the levels of trace elements and the relationship between trace elements and clinical parameters in T1D and DKA patients,and construct sex-specific auxiliary prediction model combined with trace elements and clinical parameters.Methods:In this study,a total of 105 T1D patients(43 males and 62 females,aged from 1to 21 years)with negative insulin autoantibodies were collected from January 2019 to December 2020 of the our hospital,including 47 DKA patients(23 males and 24 females,aged from 2 to 18 years);At the same time,105 healthy individuals(43males and 62 females,ranging from 1 to 21 years old)with age/sex matching the disease group from January 2019 to December 2020 were selected.Calcium(Ca),magnesium(Mg),zinc(Zn),copper(Cu),iron(Fe)and selenium(Se)in serum were determined by inductively coupled plasma mass spectrometry.Clinical biochemical index data of patients were screened and exported from the medical record system.The subjects were divided into training set and verification set with a ratio of 7:3,and the prediction model of nomograph was established by logistics regression analysis.The ROC curve,goodness of fit test,decision curve analysis and other statistical methods were used to verify the model.LMS method was used to evaluate the correlation between trace element level,diagnostic efficacy and the number of abnormal clinical indicators.Results:Compared with the healthy population,the level of serum Mg,Ca and Fe of female T1D population was lower,while the level of Fe,Zn and Cu in the serum of T1D individuals was higher than that of male healthy population.There were significant differences in serum Mg,Fe and Cu levels in T1D group(P<0.05).The serum Fe and Cu levels in males were higher than those in females,while the serum Mg levels in males were lower than those in females.The serum Mg,Ca and Zn showed low levels in DKA patients,while the serum Fe,Cu and Se levels of DKA patients showed opposite trend.There was a significant difference in serum Fe and Zn between DKA group and non DKA group.The auxiliary prediction model(triglyceride,total protein,serum magnesium)had the highest prediction efficiency in males(AUC=0.993),while the female model(apolipoprotein A,creatinine,Fe,Se,Zn/Cu ratio)had the best prediction efficiency(AUC=0.951).The model had passed the validation set,and the goodness of fit test and DCA results had confirmed its satisfactory clinical applicability.Low level of total protein(TP),serum Zn and high level of lipase were effective combinations to predict DKA(AUC of training set and verification set were 0.867 and 0.961 respectively).The levels of serum Mg and Zn in males fluctuated with the increase in numbers of abnormal clinical parameters(NACP).The serum Zn level of females increased with NACP;Serum Se in males and females increased first and then decreased with NACP.The relationship between prediction efficiency of the DKA prediction model and NACP showed the same trend.Conclusions:1.Sex-specific difference were found in serum Mg,Fe and Cu in T1D.2.The combination of triglyceride,TP and serum Mg for males,and apolipoprotein A,creatinine,Fe,Se,Zn/Cu ratio for females could effectively predict T1D in patients with negative anti-bodies,which would provide alarm for the population with high-risk of T1D and serve the T1D prediction in patients with negative anti-bodies.3.The combination of TP,lipase and Zn could predict DKA efficiently,which would benefit the early identification and prevention of DKA to improve prognosis.