The association between plasma free amino acids and type 2 diabetes mellitus complicated with infection in Chinese patients.

BACKGROUND: Type 2 diabetes mellitus (T2DM), one of the most common public diseases threatening human health, is always accompanied by infection. Though there are still a variety of flaws in the treatment of some infectious diseases, metabolomics provides a fresh perspective to explore the relationship between T2DM and infection. Our research aimed to investigate the association between plasma free amino acids (PFAAs) and T2DM complicated with infection in Chinese patients. METHODS: A cross-sectional study was conducted from May 2015 to August 2016. We retrieved the medical records of 1032 inpatients with T2DM from Liaoning Medical University First Affiliated Hospital and we used mass spectrometry to quantify 23 PFAAs. Infections contained 15 individual categories that could be retrieved from the database. Principal component analysis was used to extract factors of PFAAs. Multi-variable binary logistic regression was used to obtain odds ratios (OR) and their 95% confidence intervals (CI). RESULTS: Among 1032 inpatients,109 (10.6%) had infectious diseases. Six factors, accounting for 68.6% of the total variance, were extracted. Factor 4 consisted of Glu, Asp and Orn. Factor 5 consisted of Hcy and Pip. After adjusting for potential confounders, factor 4 was positively correlated with T2DM complicated with infection in Chinese T2DM patients (OR: 1.27, 95%CI: 1.06-1.52). Individual Hcy in factor 5 was positively associated with T2DM complicated with infection (OR: 1.33, 95%CI: 1.08-1.64). Furthermore, factor 4 (OR: 1.44, 95%CI: 1.11-1.87), Orn (OR: 1.01, 95%CI: 1.00-1.02) and Hcy (OR: 1.56, 95%CI: 1.14-3.14) were positively associated with bacterial infection in Chinese T2DM patients, while factor 5 (OR: 0.71, 95%CI: 0.50-1.00) was negatively associated with bacterial infection. CONCLUSIONS: Urea cycle-related metabolites (Orn, Asp, Glu) and Hcy were positively associated with T2DM complicated with infection in China. Orn and Hcy were positively associated with bacterial infection in T2DM patients in China.

A Metabolism-Based Interpretable Machine Learning Prediction Model for Diabetic Retinopathy Risk: A Cross-Sectional Study in Chinese Patients with Type 2 Diabetes.

The burden of diabetic retinopathy (DR) is increasing, and the sensitive biomarkers of the disease were not enough. Studies have found that the metabolic profile, such as amino acid (AA) and acylcarnitine (AcylCN), in the early stages of DR patients might have changed, indicating the potential of metabolites to become new biomarkers. We are amid to construct a metabolite-based prediction model for DR risk. This study was conducted on type 2 diabetes (T2D) patients with or without DR. Logistic regression and extreme gradient boosting (XGBoost) prediction models were constructed using the traditional clinical features and the screening features, respectively. Assessing the predictive power of the models in terms of both discrimination and calibration, the optimal model was interpreted using the Shapley Additive exPlanations (SHAP) to quantify the effect of features on prediction. Finally, the XGBoost model incorporating AA and AcylCN variables had the best comprehensive evaluation (ROCAUC = 0.82, PRAUC = 0.44, Brier score = 0.09). C18 : 1OH lower than 0.04 µmol/L, C18 : 1 lower than 0.70 µmol/L, threonine higher than 27.0 µmol/L, and tyrosine lower than 36.0 µmol/L were associated with an increased risk of developing DR. Phenylalanine higher than 52.0 µmol/L was associated with a decreased risk of developing DR. In conclusion, our study mainly used AAs and AcylCNs to construct an interpretable XGBoost model to predict the risk of developing DR in T2D patients which is beneficial in identifying high-risk groups and preventing or delaying the onset of DR. In addition, our study proposed possible risk cut-off values for DR of C18 : 1OH, C18 : 1, threonine, tyrosine, and phenylalanine.

Prevalence of Diabetic Retinopathy and Use of Common Oral Hypoglycemic Agents Increase the Risk of Diabetic Nephropathy-A Cross-Sectional Study in Patients with Type 2 Diabetes.

OBJECTIVE: This study investigated the effect of amino acid metabolism on the risk of diabetic nephropathy under different conditions of the diabetic retinopathy, and the use of different oral hypoglycemic agents. METHODS: This study retrieved 1031 patients with type 2 diabetes from the First Affiliated Hospital of Liaoning Medical University in Jinzhou, which is located in Liaoning Province, China. We conducted a spearman correlation study between diabetic retinopathy and amino acids that have an impact on the prevalence of diabetic nephropathy. Logistic regression was used to analyze the changes of amino acid metabolism in different diabetic retinopathy conditions. Finally, the additive interaction between different drugs and diabetic retinopathy was explored. RESULTS: It is showed that the protective effect of some amino acids on the risk of developing diabetic nephropathy is masked in diabetic retinopathy. Additionally, the additive effect of the combination of different drugs on the risk of diabetic nephropathy was greater than that of any one drug alone. CONCLUSIONS: We found that diabetic retinopathy patients have a higher risk of developing diabetic nephropathy than the general type 2 diabetes population. Additionally, the use of oral hypoglycemic agents can also increase the risk of diabetic nephropathy.

The Association of Homocysteine and Diabetic Retinopathy in Homocysteine Cycle in Chinese Patients With Type 2 Diabetes.

Objective: This study aimed to explore the relationship between homocysteine (Hcy) and diabetic retinopathy (DR) and the impacts of the Hcy pathway on this relationship against this background. Methods: This study retrieved 1979 patients with type 2 diabetes (T2D) from the First Affiliated Hospital of Liaoning Medical University in Jinzhou, Liaoning Province, China. Multiple logistic regression was used to analyze the effects of Hcy cycle on the relationship between Hcy and DR. Spearman's rank correlation analysis was used to analyze the correlation between risk factors related to DR progression and Hcy. Finally, the results of logistic regression were supplemented by mediation analysis. Results: We found there was a negative correlation between low concentration of Hcy and DR (OR : 0.83, 95%CI: 0.69-1). After stratifying all patients by cysteine (Cys) or Methionine (Met), this relationship remained significant only in low concentration of Cys (OR: 0.75, 95%CI: 0.61-0.94). Through the RCS curve, we found that the effect of Hcy on DR presents a U-shaped curve relationship. Mediating effect in Met and Hcy cycles was also significant [Total effect c (OR: 0.968, 95%CI: 0.938-0.998), Direct effect path c' (OR: 0.969, 95%CI: 0.940-0.999), Path a (OR: 1.047, 95%CI: 1.004-1.091), Path b (OR: 0.964, 95%CI: 0.932-0.998)]. Conclusions: The relationship between Hcy and DR presents a U-shaped curve and the homocysteine cycle pathway has an impact on it. And too low concentration of Hcy indicates a lack of other substances, such as vitamins. It is suggested that the progression of DR is the result of a combination of many risk factors. Further prospective studies are needed to determine the role of Hcy in the pathogenesis of DR.

Identification of four differentially methylated genes as prognostic signatures for stage I lung adenocarcinoma.

BACKGROUND: Lung adenocarcinoma (LUAD) is the main subtype of non-small cell lung cancer with a low survival prognosis. We aimed to generate a prognostic model for the postoperative recurrence of LUAD. METHODS: The methylated DNA data of LUAD patients were downloaded from the Cancer Genome Atlas (TCGA). The differentially methylated genes were identified and protein-protein interacting network was constructed, with which prognostic signature of this cancer was generated. Survival and functional pathways analysis w used to evaluate the clustering ability of the prognostic signature. RESULTS: We identified 151 differentially methylated genes related to relapse-free survival of patients with LUAD. Nine hub genes were identified in PPI network, with which 4 gene pair signature was selected as prognostic signature. The potential functions of 6 genes (JDP2, SERPINA5, PLG, SEMG2, RFX5, and POLR3B) in the 4-gene pair signature were enriched in intracellular protein synthesis and transportation. CONCLUSION: The four gene pair signature can predict the prognosis of patients with stage I LUAD. Our study provides a reference for patients with postoperative adjuvant therapy.

Association between XRCC1 polymorphisms and glioma risk among Chinese population.

The pathogenesis of glioma remains largely unknown now. It has been suggested that the X-ray cross-complementing group 1 (XRCC1) gene may influence the capacity to repair DNA damage leading to an increased gliomas susceptibility. This study aimed to evaluate the relationship between XRCC1 polymorphisms and glioma risk. Genotypes were assessed in 368 Chinese glioma patients and 346 healthy controls. XRCC1 Arg194Trp (rs1799782), Arg280His (rs25489) and Arg399Gln (rs25487) and three additional polymorphisms were directly sequenced. The frequency of Arg280His A allele was significant lower in glioma group than in healthy controls [9.6 vs 16%, OR=0.60 (0.46-0.80), P<0.001]; the frequencies of GA or AA genotypes were different in two groups (16.6 vs 22.8%, 1.3 vs 4.7%). The frequency of Arg399Gln A allele was significant higher in glioma group than in healthy controls [38.7 vs 30.1%, OR=1.29 (1.11-1.49), P=0.001]; the frequencies of GA or AA genotypes were different in two groups (45.4 vs 38.2%, 16 vs 10.9%). This study demonstrates that the rs25489 (Arg280His) and Arg399Gln (rs25487) polymorphisms in XRCC1 gene might influence the risk of developing glioma in Chinese population.

Use of a transanal drainage tube for prevention of anastomotic leakage and bleeding after anterior resection for rectal cancer.

BACKGROUND: The aim of the present study was to investigate the usefulness of the transanal drainage tube for prevention of anastomotic leakage and bleeding after anterior resection for rectal cancer. METHODS: Between January 2007 and May 2011 a nonrandomized prospective study of patients undergoing anterior resection for rectal cancer was done. The patients were divided into the transanal drainage tube (TDT) and non-transanal drainage tube (NTDT) groups according to whether the transanal drainage tube was used in the operation. Clinical characteristics and postoperative complications were compared between the TDT and NTDT groups. RESULTS: The study included 81 patients in the TDT group and 77 patients in the NTDT group. In the TDT group, anastomotic leakage occurred in 2 patients and no anastomotic bleeding occurred. In the NTDT group, anastomotic leakage occurred in 7 patients and anastomotic bleeding occurred in 2 patients. The TDT group had significantly fewer anastomotic complications compared with the NTDT group (2.5 vs 11.7 %; P = 0.029). Furthermore, the TDT group showed an obvious reduction in the rate of anastomotic leakage and anastomotic bleeding compared with the NTDT group (2.5 vs 7.8 % and 0.0 vs 2.6 %), but because the number of cases is relatively small, the difference did not reach statistical significance (P = 0.160 and P = 0.236). CONCLUSIONS: The use of a transanal drainage tube in anterior resection for rectal cancer may be a simple, safe, and effective means of preventing or reducing the occurrence of anastomotic leakage and bleeding. A larger-scale single or multi-center prospective randomized study or a meta-analysis including similar studies is necessary for further elucidation of this issue.

A dynamic clonal selection immune clustering algorithm.

According to the basis of clonal selection immune algorithm and hierarchical clustering, a dynamic clonal selection immune clustering algorithm is presented, which no pre-knowledge is needed. The proposed algorithm bases on antibody affinity, to recognize antigen, restrain and merge antibody. By using aiNET immune network model, the algorithm mutates location of antibodies, in which the mutating rate is dynamically adjusted with inverse proportion to the number of immune evolution generations. After dynamic mutation, the similar antibodies are merged again, and the same processes repeats until it meets the ending condition. Experimental results showed that the proposed algorithm is more coincidental reality of clustering and more preferable performance than traditional ones.