Diabetes, Metabolic Syndrome and Obesity (Oct 2023)
Evaluation of Trace Elements Levels and Construction of Auxiliary Prediction Model in Patients with Diabetes Ketoacidosis in Type 1 Diabetes
Abstract
Jiatong Chai,1 Zeyu Sun,1 Qi Zhou,2 Jiancheng Xu1 1Department of Laboratory Medicine, First Hospital of Jilin University, Changchun, People’s Republic of China; 2Department of Pediatrics, First Hospital of Jilin University, Changchun, People’s Republic of ChinaCorrespondence: Jiancheng Xu, Email [email protected]: Trace elements play an important role in reflecting physical metabolic status, but have been rarely evaluated in diabetes ketoacidosis (DKA). Since clinical biochemical parameters are the first-line diagnostic data mastered by clinical doctors and DKA has a rapid progression, it is crucial to fully utilize clinical data and combine innovative parameters to assist in assessing disease progression. The aim of this study was to evaluate the levels of trace elements in DKA patients, followed by construction of predictive models combined with the laboratory parameters.Methods: A total of 96 T1D individuals (48 DKA patients) were collected from the First Hospital of Jilin University. Serum calcium (Ca), magnesium (Mg), zinc (Zn), copper (Cu), iron (Fe) and selenium (Se) were measured by Inductively Coupled Plasma Mass Spectrometry, and the data of biochemical parameters were collected from the laboratory information system. Training and validation sets were used to construct the model and examine the efficiency of the model. The lambda-mu-sigma method was used to evaluate the changes in the model prediction efficiency as the severity of the patient’s condition increases.Results: Lower levels of serum Mg, Ca and Zn, but higher levels of serum Fe, Cu and Se were found in DKA patients. Low levels of total protein (TP), Zn and high levels of lipase would be an efficient combination for the prediction of DKA (Area under curves for training set and validation set were 0.867 and 0.961, respectively). The examination test confirmed the clinical applicability of the constructed models. The increasing predictive efficiency of the model was found with NACP.Conclusion: More severe oxidative stress in DKA led to further imbalance of trace elements. The combination of TP, lipase and Zn could predict DKA efficiently, which would benefit the early identification and prevention of DKA to improve prognosis.Keywords: type 1 diabetes, diabetes ketoacidosis, diabetic complication, trace elements, prediction model
