Transient paper-based electrochemical biosensor Fabricated by superadditive Cu-TCPP(Fe)/Mxene for Multipathway non-invasive, highly sensitive detection of Bodily metabolites.

Current advances in non-invasive fluid diagnostics highlight unique benefits for monitoring metabolic diseases. However, the low concentrations and complex compositions of biomarkers in fluids such as sweat, urine, and saliva impose stringent demands on the sensitivity and stability of detection technologies. Here, we developed a high-sensitivity, low-cost instantaneous electrochemical sensor based on the superadditive effect mechanism of Cu-TCPP(Fe)/Mxene (MMs Paper-ECL Sensor), which has been successfully applied for the simultaneous real-time detection of glucose and uric acid. Strong interfacial interactions between Mxene and Cu-TCPP(Fe) were revealed through precise simulation calculations and multi-dimensional characterization analysis, significantly enhancing the sensor's electrocatalytic performance and reaction kinetics. Experimentally, this exceptional electrocatalytic activity was demonstrated in its unprecedented high sensitivity and wide linear detection range for glucose and uric acid, with a non-invasive linear range from 0.001 nM to 5 mM, 0.025 nM-5 mM, detection limits as low as 1.88 aM and 5.80 pM, and stability extending up to 100 days. This represents not only a breakthrough in sensitivity and stability but also provides an effective, low-cost solution that overcomes the limitations of existing electronic devices, enabling multi-channel simultaneous detection. The universality of this sensor holds vast potential for application in the field of non-invasive fluid diagnostics.

Interpretable machine learning identifies metabolites associated with glomerular filtration rate in type 2 diabetes patients.

Objective: The co-occurrence of kidney disease in patients with type 2 diabetes (T2D) is a major public health challenge. Although early detection and intervention can prevent or slow down the progression, the commonly used estimated glomerular filtration rate (eGFR) based on serum creatinine may be influenced by factors unrelated to kidney function. Therefore, there is a need to identify novel biomarkers that can more accurately assess renal function in T2D patients. In this study, we employed an interpretable machine-learning framework to identify plasma metabolomic features associated with GFR in T2D patients. Methods: We retrieved 1626 patients with type 2 diabetes (T2D) in Liaoning Medical University First Affiliated Hospital (LMUFAH) as a development cohort and 716 T2D patients in Second Affiliated Hospital of Dalian Medical University (SAHDMU) as an external validation cohort. The metabolite features were screened by the orthogonal partial least squares discriminant analysis (OPLS-DA). We compared machine learning prediction methods, including logistic regression (LR), support vector machine (SVM), random forest (RF), and eXtreme Gradient Boosting (XGBoost). The Shapley Additive exPlanations (SHAP) were used to explain the optimal model. Results: For T2D patients, compared with the normal or elevated eGFR group, glutarylcarnitine (C5DC) and decanoylcarnitine (C10) were significantly elevated in GFR mild reduction group, and citrulline and 9 acylcarnitines were also elevated significantly (FDR<0.05, FC > 1.2 and VIP > 1) in moderate or severe reduction group. The XGBoost model with metabolites had the best performance: in the internal validate dataset (AUROC=0.90, AUPRC=0.65, BS=0.064) and external validate cohort (AUROC=0.970, AUPRC=0.857, BS=0.046). Through the SHAP method, we found that C5DC higher than 0.1µmol/L, Cit higher than 26 µmol/L, triglyceride higher than 2 mmol/L, age greater than 65 years old, and duration of T2D more than 10 years were associated with reduced GFR. Conclusion: Elevated plasma levels of citrulline and a panel of acylcarnitines were associated with reduced GFR in T2D patients, independent of other conventional risk factors.

Identification of UDP-glucuronosyltransferase (UGT) isoforms involved in the metabolism of Chlorophenols (CPs).

Chlorophenols (CPs) are a group of pollutants that pose a great threat to the environment, they are widely used in industrial and agricultural wastes, pesticides, herbicides, textiles, pharmaceuticals and plastics. Among CPs, pentachlorophenol was listed as one of the persistent organic pollutants (POPs) by the Stockholm convention. This study aims to identify the UDP-glucosyltransferase (UGT) isoforms involved in the metabolic elimination of CPs. CPs' mono-glucuronide was detected in the human liver microsomes (HLMs) incubation mixture with co-factor uridine-diphosphate glucuronic acid (UDPGA). HLMs-catalyzed glucuronidation metabolism reaction equations followed Michaelis-Menten or substrate inhibition type. Recombinant enzymes and chemical reagents inhibition experiments were utilized to phenotype the main UGT isoforms involved in the glucuronidation of CPs. UGT1A6 might be the major enzyme in the glucuronidation of mono-chlorophenol isomer. UGT1A1, UGT1A6, UGT1A9, UGT2B4 and UGT2B7 were the most important five UGT isoforms for metabolizing the di-chlorophenol and tri-chlorophenol isomers. UGT1A1 and UGT1A3 were the most important UGT isoforms in the catalysis of tetra-chlorophenol and pentachlorophenol isomers. Species differences were investigated using rat liver microsomes (RLMs), pig liver microsomes (PLMs), dog liver microsomes (DLMs), and monkey liver microsomes (MyLMs). All these results were helpful for elucidating the metabolic elimination and toxicity of CPs.

Longitudinal association between soft drink consumption and handgrip strength in adults: a prospective analysis from the Tianjin Chronic Low-Grade Systemic Inflammation and Health (TCLSIH) cohort study.

Soft drink consumption has become a highly controversial public health issue. Given the pattern of consumption in China, sugar-sweetened beverage is the main type of soft drink consumed. Due to containing high levels of fructose, a soft drink may have a deleterious effect on handgrip strength (HGS) due to oxidative stress, inflammation and insulin resistance. However, few studies show an association between soft drink consumption and HGS in adults. We aimed to investigate the association between soft drink consumption and longitudinal changes in HGS among a Chinese adult population. A longitudinal population-based cohort study (5-year follow-up, median: 3·66 years) was conducted in Tianjin, China. A total of 11 125 participants (56·7 % men) were enrolled. HGS was measured using a handheld digital dynamometer. Soft drink consumption (mainly sugar-containing carbonated beverages) was measured at baseline using a validated FFQ. ANCOVA was used to evaluate the association between soft drink consumption and annual change in HGS or weight-adjusted HGS. After adjusting for multiple confounding factors, the least square means (95 % CI) of annual change in HGS across soft drink consumption frequencies were -0·70 (-2·49, 1·09) for rarely drinks, -0·82 (-2·62, 0·97) for < 1 cup/week and -0·86 (-2·66, 0·93) for ≥ 1 cup/week (Pfor trend < 0·05). Likewise, a similar association was observed between soft drink consumption and annual change in weight-adjusted HGS. The results indicate that higher soft drink consumption was associated with faster HGS decline in Chinese adults.

New Perspectives on the Risks of Hydroxylated Polychlorinated Biphenyl (OH-PCB) Exposure: Intestinal Flora α-Glucosidase Inhibition.

Polychlorinated biphenyls (PCBs) are a group of colorless and odorless environmental pollutants with a wide range of toxic effects. Some PCBs, especially less chlorinated ones, will rapidly undergo phase I metabolism after entering the body, and hydroxylated polychlorinated biphenyls (OH-PCBs) are the main metabolites of PCBs. Intestinal flora α-glucosidase is a common carbohydrate-active enzyme which is ubiquitous in human intestinal flora. It can convert complex dietary polysaccharides into monosaccharides, assisting the body in degrading complex carbohydrates and providing energy for the survival and growth of bacterial flora. The present study aims to investigate the inhibition of the activity of intestinal flora α-glucosidase by OH-PCBs. 4-Nitrophenyl-α-D-glucopyranoside (PNPG) was used as a probe substrate for α-glucosidase, and in vitro incubation experiments were conducted to study the inhibition of 26 representative OH-PCBs on α-glucosidase. Preliminary screening of in vitro incubation was performed with 100 µM of OH-PCBs. The results showed that 26 OH-PCBs generally exhibited strong inhibition of α-glucosidase. The concentration-dependent inhibition and half inhibition concentrations (IC50s) of OH-PCBs on α-glucosidase were determined. 4'-OH-PCB 86 and 4'-OH-PCB 106 were chosen as representative OH-PCBs, and the inhibition kinetic parameters (Kis) of inhibitors for α-glucosidase were determined. The inhibition kinetic parameters (Kis) of 4'-OH-PCB 86 and 4'-OH-PCB 106 for α-glucosidase are 1.007 µM and 0.538 µM, respectively. The silico docking method was used to further analyze the interaction mechanism between OH-PCBs and α-glucosidase. All these results will help us to understand the risks of OH-PCB exposure from a new perspective.

Effect of vegetable consumption on risk of gastric cancer: a systematic review and multi-level meta-analysis of prospective studies.

Vegetables are known to be beneficial to human health, but the association between vegetable consumption and gastric cancer remains uncertain. To synthesise knowledge about the relationship between vegetable group consumption and gastric cancer risk, update present meta-analyses and estimate associations between vegetable consumption and gastric cancer risk based solely on prospective studies, we perform a PRISMA-compliant three-level meta-analysis. Systematic search identified thirteen prospective studies with fifty-two effect sizes that met all inclusion criteria and no exclusion criteria for our meta-analysis. Pooled risk ratios (RRs) showed a positive association between high vegetable consumption and low gastric cancer risk (pooled RR 0·93, 95% confidence interval 0·90-0·97, P = 0·06). In moderator analyses for indicators of gender, region and quantity of vegetable intake, there was no significant difference between subgroups. However, the effect became significant in populations with lower than the minimum risk exposure level (TMREL) of vegetable consumption (P < 0·05). Higher vegetable intake is associated with a decreased risk of gastric cancer. This effect may be limited to specific populations, such as ones with lower vegetable consumption. Evidence from our study has important public health implications for dietary recommendations.

Explainable Machine Learning-Based Prediction Model for Diabetic Nephropathy.

The aim of this study is to analyze the effect of serum metabolites on diabetic nephropathy (DN) and predict the prevalence of DN through a machine learning approach. The dataset consists of 548 patients from April 2018 to April 2019 in the Second Affiliated Hospital of Dalian Medical University (SAHDMU). We select the optimal 38 features through a least absolute shrinkage and selection operator (LASSO) regression model and a 10-fold cross-validation. We compare four machine learning algorithms, including extreme gradient boosting (XGB), random forest, decision tree, and logistic regression, by AUC-ROC curves, decision curves, and calibration curves. We quantify feature importance and interaction effects in the optimal predictive model by Shapley additive explanation (SHAP) method. The XGB model has the best performance to screen for DN with the highest AUC value of 0.966. The XGB model also gains more clinical net benefits than others, and the fitting degree is better. In addition, there are significant interactions between serum metabolites and duration of diabetes. We develop a predictive model by XGB algorithm to screen for DN. C2, C5DC, Tyr, Ser, Met, C24, C4DC, and Cys have great contribution in the model and can possibly be biomarkers for DN.

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.

Serum mannan-binding lectin-associated serine proteases in early pregnancy for gestational diabetes in Chinese pregnant women.

Aims: This study aimed to explore associations of mannan-binding lectin-associated serine protease (MASP) levels in early pregnancy with gestational diabetes mellitus (GDM). We also examined interactions of MASPs and deoxycholic acid (DCA)/glycoursodeoxycholic acid (GUDCA) for the GDM risk and whether the interactive effects if any on the GDM risk were mediated via lysophosphatidylcholine (LPC) 18:0. Materials and methods: A 1:1 case-control study (n = 414) nested in a prospective cohort of pregnant women was conducted in Tianjin, China. Binary conditional logistic regressions were performed to examine associations of MASPs with the GDM risk. Additive interaction measures were used to examine interactions between MASPs and DCA/GUDCA for the GDM risk. Mediation analyses and Sobel tests were used to examine mediation effects of LPC18:0 between the copresence of MASPs and DCA/GUDCA on the GDM risk. Results: High MASP-2 was independently associated with GDM [odds ratio (OR): 2.62, 95% confidence interval (CI): 1.44-4.77], while the effect of high MASP-1 on GDM was attributable to high MASP-2 (P for Sobel test: 0.003). Low DCA markedly increased the OR of high MASP-2 alone from 2.53 (1.10-5.85) up to 10.6 (4.22-26.4), with a significant additive interaction. In addition, high LPC18:0 played a significant mediating role in the links from low DCA to GDM and from the copresence of high MASP-2 and low DCA to GDM (P for Sobel test <0.001) but not in the link from high MASP-2 to GDM. Conclusions: High MASP-1 and MASP-2 in early pregnancy were associated with GDM in Chinese pregnant women. MASP-2 amplifies the risk of low DCA for GDM, which is mediated via LPC18:0.

Serum Sulfur-Containing Amino Acids and Risk of Maternal Gestational Diabetes and Adverse Growth Patterns in Offspring.

BACKGROUND: To estimate associations of sulfur-containing amino acids (SAAs) in the early trimester of pregnancy and gestational diabetes mellitus (GDM) and estimate associations of maternal SAAs with adverse growth patterns in offspring. METHODS: We established a 1:1 matched case-control study (n = 486) from our cohort of pregnant women, and 401 children were followed up at ages 1 to 8 years. We conducted binary conditional logistic regression to estimate the risk associations of serum SAAs with GDM. Multinomial logistic regression was implemented to explore associations of maternal SAAs with adverse growth patterns in the offspring. RESULTS: High serum methionine and cystine were independently associated with increased GDM risk (OR: 1.92, 95%CI: 1.18-3.13 and 2.69, 1.59-4.53). Conversely, a low level of serum taurine was independently associated with increased GDM risk (2.61, 1.64-4.16). Maternal high cystine and low taurine were also associated with an increased risk of persistent obesity growth pattern (POGP) in offspring (OR: 2.79, 95%CI: 1.09-7.17 and 3.92, 1.11-13.89) and the effect was largely independent of GDM. CONCLUSIONS: High serum methionine, cystine and low serum taurine in the early trimester of pregnancy were associated with a greatly increased risk of GDM. Maternal high cystine and low taurine were associated with elevated risk of offspring POGP, largely independent of GDM.

Circulating hyaluronidase in early pregnancy and increased risk of gestational diabetes in Chinese pregnant women: A nested case control study.

BACKGROUND AND AIMS: To explore association of serum hyaluronidase 1 (HYAL1) level in early pregnancy with gestational diabetes mellitus (GDM), and to examine interactive effects of HYAL1 with ceramides species on GDM risk. MATERIALS AND METHODS: We conducted a 1:1 matched case-control study (n = 414) of pregnant women from 2010 to 2012 in Tianjin, China. Blood samples were collected at the first antenatal care visit (at a median of 10th gestational weeks). Binary conditional logistic regression and restricted cubic spline (RCS) analysis were used to examine full-range risk association between HYAL1 and GDM. Additive interactions and multiplicative interactions were employed to test interactive effects of HYAL1 with ceramides species on GDM risk. RESULTS: Ln HYAL1 was linearly associated with GDM risk and the adjusted OR of HYAL1 ≥ vs. < its median for GDM was significant (1.65, 95%CI: 1.08-2.52). High HYAL1 markedly enhanced the ORs of high ceramide 18:0 for GDM from 2.31 (1.06-5.01) to 6.74 (2.85-16.0), and low ceramide 24:0 from 3.08 (1.33-7.11) to 8.15 (3.03-21.9), with significant additive interactions. CONCLUSIONS: High HYAL1 in early pregnancy may increase the risk of GDM in Chinese women, possibly via enhancing the effects of high ceramide 18:0 and low ceramide 24:0 on GDM risk.

Effect of passive smoking exposure on risk of type 2 diabetes: a systematic review and meta-analysis of prospective cohort studies.

Objective: The effect of passive smoking exposure on the risk of type 2 diabetes has not been systematically studied. A meta-analysis was conducted to assess the association between passive smoking exposure and the risk of diabetes. Methods: We searched three major databases up to 31 October 2022 to identify relevant prospective cohort studies on the association between passive smoking and the risk of type 2 diabetes. The pooled relative risk (RR) and 95% confidence interval (CI) for the association between passive smoking exposure and the risk of type 2 diabetes were analyzed using a fixed-effect model. Results: Ten prospective cohort studies were included in this meta-analysis, with a total of 251,620 participants involved. The pooled RR showed a significantly positive association between nonsmokers exposed to passive smoking and type 2 diabetes as compared to non-smokers who were not exposed to passive smoking [RR = 1.27; 95% CI (1.19, 1.36); p < 0.001]. Sensitivity analysis indicated that the pooled RR was not substantially affected by any of the individual studies. Conclusion: Exposure to passive smoking increases the risk of type 2 diabetes. This study may have a positive effect on the prevention of type 2 diabetes. Systematic review registration: https://www.crd.york.ac.uk/PROSPERO/, identifier CRD42023372532.

Association between soft drink consumption and carotid atherosclerosis in a large-scale adult population: The TCLSIH cohort study.

BACKGROUND AND AIMS: Carotid atherosclerosis indicates an increased risk for cardiac-cerebral vascular disease. Given the pattern of consumption in China, sugar-sweetened beverage is the main type of soft drink consumed. As soft drinks contain a high amount of fructose, they may be a risk factor of carotid atherosclerosis. A prospective cohort study was conducted to investigate the association between soft drink consumption and the incidence of carotid atherosclerosis in a Chinese adult population. METHODS AND RESULTS: A total of 3828 participants (men: 2007 and women: 1821) were included. Carotid atherosclerosis was measured by using ultrasonography and was defined by increased carotid intima-media thickness and/or carotid plaques. Soft drink consumption was assessed using a validated food frequency questionnaire. Cox proportional hazards regression analysis was used to assess the association of soft drink consumption categories with the incidence of carotid atherosclerosis. During a mean follow-up of 3.20 years, 1009 individuals of the 3828 eligible participants developed carotid atherosclerosis. After adjusting for potential confounding factors, we compared the higher levels to the lowest level of soft drink consumption in women, and we estimated the multivariable hazard ratios and 95% confidence intervals of incident carotid atherosclerosis to be 1.09 (0.80, 1.50), and 1.56 (1.14, 2.13) (P for trend <0.05). However, there was no significant association between soft drink consumption and the incidence of carotid atherosclerosis in men or total population. CONCLUSION: The result indicated that soft drink consumption was associated with a higher incidence of carotid atherosclerosis in women. TRIAL REGISTERED: UMIN Clinical Trials Registry. TRIAL REGISTRATION NUMBER: UMIN000027174. TRIAL REGISTRATION WEBSITE: https://upload.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000031137.

Aromatic Amino Acids and Their Interactions with Gut Microbiota-Related Metabolites for Risk of Gestational Diabetes: A Prospective Nested Case-Control Study in a Chinese Cohort.

INTRODUCTION: The aim of this study was to explore associations of aromatic amino acids (AAA) in early pregnancy with gestational diabetes mellitus (GDM), and whether high AAA and gut microbiota-related metabolites had interactive effects on GDM risk. METHODS: We conducted a 1:1 case-control study (n = 486) nested in a prospective cohort of pregnant women from 2010 to 2012. According to the International Association of Diabetes and Pregnancy Study Group's criteria, 243 women were diagnosed with GDM. Binary conditional logistic regression was performed to examine associations of AAA with GDM risk. Interactions between AAA and gut microbiota-related metabolites for GDM were examined using additive interaction measures. RESULTS: High phenylalanine and tryptophan were associated with increased GDM risk (OR: 1.72, 95% CI: 1.07-2.78 and 1.66, 1.02-2.71). The presence of high trimethylamine (TMA) markedly increased the OR of high phenylalanine alone up to 7.95 (2.79-22.71), while the presence of low glycoursodeoxycholic acid (GUDCA) markedly increased the OR of high tryptophan alone up to 22.88 (5.28-99.26), both with significant additive interactions. Furthermore, high lysophosphatidylcholines (LPC18:0) mediated both interactive effects. CONCLUSIONS: High phenylalanine may have an additive interaction with high TMA, while high tryptophan may have an additive interaction with low GUDCA toward increased risk of GDM, both being mediated via LPC18:0.

The association and prediction value of acylcarnitine on diabetic nephropathy in Chinese patients with type 2 diabetes mellitus.

BACKGROUND: Acylcarnitines play a role in type 2 diabetes mellitus (T2DM), but the relationship between acylcarnitine and diabetic nephropathy was unclear. We aimed to explore the association of acylcarnitine metabolites with diabetic nephropathy and estimate the predictive value of acylcarnitine for diabetic nephropathy. METHODS: A total of 1032 (mean age: 57.24 ± 13.82) T2DM participants were derived from Liaoning Medical University First Affiliated Hospital. Mass Spectrometry was utilized to measure levels of 25 acylcarnitine metabolites in fasting plasma. Diabetic nephropathy was ascertained based on the medical records. Factor analysis was used to reduce the dimensions and extract factors of the 25 acylcarnitine metabolites. Logistic regression was used to estimate the relationship between factors extracted from the 25 acylcarnitine metabolites and diabetic nephropathy. Receiver operating characteristic curves were used to test the predictive values of acylcarnitine factors for diabetic nephropathy. RESULTS: Among all T2DM participants, 138 (13.37%) patients had diabetic nephropathy. Six factors were extracted from 25 acylcarnitines, which account for 69.42% of the total variance. In multi-adjusted logistic regression models, the odds ratio (OR, 95% confidence interval [CI]) of diabetic nephropathy on factor 1 (including butyrylcarnitine/glutaryl-carnitine/hexanoylcarnitine/octanoylcarnitine/decanoylcarnitine/lauroylcarnitine/tetradecenoylcarnitine), factor 2 (including propionylcarnitine/palmitoylcarnitine/hydroxypalmitoleyl-carnitine/octadecanoylcarnitine/arachidiccarnitine), and factor 3 (including tetradecanoyldiacylcarnitine/behenic carnitine/tetracosanoic carnitine/hexacosanoic carnitine) were 1.33 (95%CI 1.12-1.58), 0.76 (95%CI 0.62-0.93), and 1.24 (95%CI 1.05-1.47), respectively. The area under the curve for diabetic nephropathy prediction was significantly increased after the complement of factors 1, 2, and 3 in traditional factors model (P < 0.01). CONCLUSIONS: Some plasma acylcarnitine metabolites extracted in factors 1 and 3 were higher in diabetic nephropathy, while factor 2 was lower in diabetic nephropathy among T2DM patients. The addition of acylcarnitine to traditional factors model improved the predictive value for diabetic nephropathy.

Relationship between plasma glutamate and cardiovascular disease risk in Chinese patients with type 2 diabetes mellitus by gender.

Objectives: This study aimed to assess the association between plasma glutamate (Glu) and the risk of cardiovascular disease (CVD) in patients with type 2 diabetes mellitus (T2DM) and whether this association differs by gender. Material and methods: We retrieved clinical information on 1032 consecutive patients with T2DM from a same tertiary care center from May 2015 to August 2016. Glu was quantified by liquid chromatography-tandem mass spectrometry analysis. Glu was converted into a categorical variable based on the median concentration in the whole population, while logistic regression was used to obtain the odds ratio (OR) and 95% confidence interval (CI), and the correlation between Glu and various biochemical indices was analyzed. Results: We found that Glu was positively associated with the risk of CVD in patients with T2DM. This correlation was more significant in women. In T2DM patients, the higher the age, body mass index (BMI), weight and systolic blood pressure (SBP), the lower the glycosylated hemoglobin (HbA1C) concentration and the higher the Glu. In female patients, the correlation between age, weight, BMI, SBP, and plasma Triglycerides (TG), and Glu was also statistically significant. Conclusion: In conclusion, female T2DM patients with high levels of Glu have a higher risk of developing CVD.

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.

Associations of serum amino acids related to urea cycle with risk of chronic kidney disease in Chinese with type 2 diabetes.

Objective: Serum levels of amino acids related to urea cycle are associated with risk of type 2 diabetes mellitus (T2DM). Our study aimed to explore whether serum levels of amino acids related to urea cycle, i.e., arginine, citrulline, and ornithine, are also associated with increased risk of chronic kidney disease (CKD) in T2DM. Methods: We extracted medical records of 1032 consecutive patients with T2DM from the Electronic Administrative System of Liaoning Medical University First Affiliated Hospital (LMUFAH) system from May 2015 to August 2016. Of them, 855 patients with completed data available were used in the analysis. CKD was defined as estimated glomerular filtration rate (eGFR) <60 mL/min/1.73 m2. Serum amino acids were measured by mass spectrometry (MS) technology. Binary logistic regression was performed to obtain odds ratios (ORs) and their 95% confidence intervals (CIs). Results: 52.3% of the 855 T2DM patients were male, and 143 had CKD. In univariable analysis, high serum citrulline, high ratio of arginine to ornithine, and low ratio of ornithine to citrulline were associated with markedly increased risk of CKD (OR of top vs. bottom tertile: 2.87, 95%CI, 1.79-4.62 & 1.98, 95%CI,1.25-3.14 & 2.56, 95%CI, 1.61-4.07, respectively). In multivariable analysis, the ORs of citrulline and ornithine/citrulline ratio for CKD remained significant (OR of top vs. bottom tertile: 2.22, 95%CI, 1.29-3.82 & 2.24, 1.29-3.87, respectively). Conclusions: In Chinese patients with T2DM, high citrulline and low ornithine/citrulline ratio were associated with increased risk of CKD.