Evaluation Among Trace Elements, Clinical Parameters and Type 1 Diabetes According to Sex: A New Sight of Auxiliary Prediction in Negative Insulin Auto-antibodies Population

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 most significant characteristic, insulin auto-antibodies, could not be found in all T1D patients, which needed the auxiliary prediction of clinical parameters. And it would benefit the early detection and treatment if some high-risk groups of T1D could predict and prevent the occurrence of disease through common clinical parameters. Hence, there was an urgent need to construct more effective and scientific statistical prediction models to serve clinic better. This study aimed to evaluate the sex-specific levels of trace elements and the relationship between trace elements and clinical parameters in T1D, and construct sex-specific auxiliary prediction model combined with trace elements and clinical parameters. A total of 105 T1D patients with negative insulin auto-antibodies and 105 age/sex-matched healthy individuals were enrolled in First Hospital of Jilin University. Inductively Coupled Plasma Mass Spectrometry was performed for the measurement of calcium (Ca), magnesium (Mg), zinc (Zn), copper (Cu), iron (Fe), selenium (Se) in the serum, and the data of clinical parameters were received from medical record system. The lambda-mu-sigma method was used to evaluate the relationship between abnormal clinical parameters and trace elements. Training set and validation set were divided for the construction of predictable models in males and females: clinical parameters model, trace element model and the combined model (clinical parameters and trace elements). Goodness fit test, decision curve analysis and other related statistical methods were used to perform data analysis. Lower levels of Mg, Ca, Fe in the serum were found in T1D population in females compared with healthy population, while levels of Fe, Zn and Cu of serum in T1D individuals were higher than those of healthy population in males. Levels of serum Mg, Fe and Cu in T1D group were found with significant sex difference for (P<0.05), and the levels of Fe and Cu in serum of males were higher than those of females, level of serum Mg in males was lower than those of females. Levels of serum Mg and Zn showed fluctuation trend with increased numbers of abnormal clinical parameters (NACP) in males. Serum Zn in females showed consistent elevated trend with NACP; serum Se increased first and then decreased with NACP in males and females. The auxiliary prediction model (Triglyceride, Total protein, serum Mg) was found with the highest predicted efficiency in males (AUC=0.993), while the model in females (Apolipoprotein A, Creatinine, Fe, Se, Zn/Cu ratio) showed the best predicted efficiency (AUC=0.951). The models had passed the verification in validation set, and Chi-square goodness-of-fit test, DCA results both confirmed their satisfactory clinical applicability. Sex-specific difference were found in serum Mg, Fe and Cu in T1D. The combination of triglyceride, total protein 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.