Evaluation of Trace Elements Levels and Construction of Auxiliary Prediction Model in Patients with Diabetes Ketoacidosis in Type 1 Diabetes.

Background: 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.

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.

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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.

Establishment of gender- and age-specific reference intervals for serum liver function tests among the elderly population in northeast China: a retrospective study.

Introduction: Reference intervals (RIs) for younger population may not apply to the elderly population. The aim of this study was to establish gender- and age-specific RIs for serum liver function tests among the elderly population and to compare with younger population RIs currently used in China and other countries. Materials and methods: This was a retrospective study, and subjects (≥ 18 year-old) were recruited from the laboratory information system (LIS) at the First Hospital of Jilin University between April 2020 and April 2021. The following parameters were collected: aspartate aminotransferase (AST), alanine aminotransferase (ALT), gamma-glutamyltransferase (GGT), alkaline phosphatase (ALP), total protein (TP), albumin (ALB), total bilirubin (TBIL), and direct bilirubin (DBIL). The Tukey method was used to eliminate outliers. Reference intervals were established by the nonparametric method. Results: A total of 23,597 healthy individuals were enrolled in the study. From all parameters AST, ALT, TP and ALB required no gender partition, while ALT, GGT, TP, ALB and DBIL required different partitions for age. Activities and concentrations of ALT, ALB, and TP showed a downward trend in the elderly aged 60-89. In contrast, DBIL showed a gradual upward trend. Conclusion: The RIs for liver function tests among healthy elderly population were different from those among young population in China. There were apparent gender and age differences in the RIs of liver function for elderly and significant differences compared with national standards and RIs in other countries. Therefore, it is necessary to establish gender- and age-specific RIs for serum liver function tests among the elderly population.

An indirect approach for reference intervals establishment of immunoglobulin A, G and M: the combination of laboratory database and statistics.

Immunoglobulins are affected by sex, age and region, so it is necessary to establish suitable reference intervals (RIs) for clinical diagnosis. Various statistical methods were used to calculate RIs, but there has been a lack of comparison among the methods. Research based on immunoglobulin RIs establishment with various methods would provide a methodological basis for further research. A total of 16,525 individuals were enrolled in the study. Individuals were selected in the medical examination center of First Hospital of Jilin University from 2014 to 2020. The lambda-mu-sigma (LMS) method was performed to evaluate the dynamic changes in analytes. RIs were calculated by parametric, non-parametric, Hoffman method and Bhattacharya method. Sex and age partitions were found for immunoglobulins G and immunoglobulin M. The levels of IgM showed no difference with age in males, but showed differences after 50 years of age in females. Circulating immunoglobulin A concentrations showed an increasing trend with age, and immunoglobulin M showed a fluctuating trend with age. Obvious difference (>5%) was commonly found among the four methods, however, the RIs established by the four methods all passed the verification with a high passing rate. Sex and age differences should be considered for immunoglobulins G and immunoglobulin M in clinical practice. The feasibility of the four indirect methods was proven, which would provides a methodological reference for further studies and benefit the application of clinical data.

A Contemporary Insight of Metabolomics Approach for Type 1 Diabetes: Potential for Novel Diagnostic Targets.

High-throughput omics has been widely applied in metabolic disease, type 1 diabetes (T1D) was one of the most typical diseases. Effective prevention and early diagnosis are very important because of infancy and persistent characteristics of T1D. The occurrence and development of T1D is a chronic and continuous process, in which the production of autoantibodies (ie serum transformation) occupies the central position. Metabolomics can evaluate the metabolic characteristics of serum before seroconversion, the changes with age and T1D complications. And the addition of natural drug metabolomics is more conducive to the systematic and comprehensive diagnosis and treatment of T1D. This paper reviewed the metabolic changes and main pathogenesis from pre-diagnosis to treatment in T1D. The metabolic spectrum of significant abnormal energy and glucose-related metabolic pathway, down-regulation of lipid metabolism and up-regulation of some antioxidant pathways has appeared before seroconversion, indicating that the body has been in the dual state of disease progression and disease resistance before T1D onset. Some metabolites (such as methionine) are closely related to age, and the types of autoantibodies produced are age-specific. Some metabolites may jointly predict DN with eGFR, and metabolomics can further contribute to the pathogenesis based on the correlation between DN and DR. Many natural drug components have been proved to act on abnormal metabolic pathways of T1D and have a positive impact on some metabolic levels, which is very important for further finding therapeutic targets and developing new drugs with small side effects. Metabolomics can provide auxiliary value for the diagnosis of T1D and provide a new direction to reveal the pathogenesis of T1D and find new therapeutic targets. The development of T1D metabolomics shows that high-throughput research methods are expected to be introduced into clinical practice.

The indirect method in the establishment of reference intervals for complement 3 and complement 4: A retrospective study.

Objective: The establishment of reference intervals (RIs) for complement 3 (C3) and complement 4 (C4) is rare, especially by indirect methods. Therefore, this study aims to establish regional RIs for C3 and C4 by an indirect method, using relevant statistical methods. Methods: Total of 12,313 data points for C3 and 12,125 data points for C4 were obtained from the First Hospital of Jilin University's database in China and standardised using the Tukey and Box-Cox statistical methods. The coefficients of the skewness-median-coefficient of variation curves (LMS) were used to determine the critical value for age, and a subsequent z test used to compare the differences. A non-parametric method was used to establish the RIs. Results: The C3 and C4 concentrations showed no significant differences by sex, and a weak correlation with age. No significant difference was found after calculating the z value for the age points on the LMS curves. The RIs for C3 and C4 were 0.83-1.58 g/L and 0.15-0.40 g/L, respectively. The RIs all passed verification. Conclusion: Suitable RIs for C3 and C4 were established for the local population, and will benefit clinical diagnosis. The feasibility and practicability of the indirect method were demonstrated.