Aim: The effect of dietary salt intake on the risk of gastric cancer is not clear. A meta-analysis was performed to estimate the association between dietary salt intake and the risk of gastric cancer. Methods: Three major databases were searched to retrieve case-control studies published in English before 1 July 2022. Random effects model analysis was used to obtain the pooled odds ratios (ORs) and 95% confidence intervals (CIs) of the association between dietary salt intake and risk of gastric cancer. Subgroup analyses were used to identify possible sources of heterogeneity. Results: Thirty-eight case-control studies were included in this meta-analysis (total population: n = 37,225). The pooled ORs showed a significantly positive association between high salt intake and gastric cancer compared with low salt intake (OR = 1.55, 95% CI (1.45, 1.64); p < 0.001). In subgroup meta-analysis for geographic region, estimation method for dietary salt intake and the source of controls, this association was not changed. Conclusion: Higher dietary salt intake increased the risk of gastric cancer. This study has implications for the prevention of gastric cancer.

Objective: The association between acylcarnitine metabolites and cardiovascular disease (CVD) in type 2 diabetes mellitus (T2DM) remains uncertain. This study aimed to investigate associations between acylcarnitines and CVD in Chinese patients with T2DM. Methods: A cross-sectional study was conducted from May 2015 to August 2016. Medical records of 741 patients with T2DM were retrieved from the main electronic database of Liaoning Medical University First Affiliated Hospital. CVD was defined as having either coronary artery disease (CAD) or heart failure (HF) or stroke. Mass Spectrometry was utilized to measure levels of 25 acylcarnitine metabolites in fasting plasma. Factor analysis was used to reduce the dimensions and extracted factors of the 25 acylcarnitine metabolites. Multivariable binary logistic regression was used to obtain odds ratios (OR) of the factors extracted from the 25 acylcarnitine metabolites and their 95% confidence intervals (CI) for CVD. Results: Of the 741 patients with T2DM, 288 had CVD. Five factors were extracted from the 25 acylcarnitines and they accounted for 65.9% of the total variance. Factor 1 consisted of acetylcarnitine, butyrylcarnitine, hydroxylbutyrylcarnitine, glutarylcarnitine, hexanoylcarnitine, octanoylcarnitine, and tetradecanoyldiacylcarnitine. Factor 2 consisted of decanoylcarnitine, lauroylcarnitine, myristoylcarnitine, 3-hydroxyl-tetradecanoylcarnitine, tetradecenoylcarnitine, and 3-hydroxypalmitoylcarnitine. After adjusting for potential confounders, increased factor 1 and 2 were associated with increased risks of CVD in T2DM (OR of factor 1: 1.45, 95% CI: 1.03–2.03; OR of factor 2: 1.23, 95% CI: 1.02–1.50). Conclusions: Elevated plasma levels of some acylcarnitine metabolites, i.e., those extracted into factor 1 and 2, were associated with CVD risk in T2DM.

Bile acid metabolism plays an important role in metabolism but it is uncertain whether bile acid metabolites in early pregnancy are associated with risk of gestational diabetes mellitus (GDM). We organized a 1:1 case-control study nested in a prospective cohort of 22,302 pregnant women recruited from 2010 to 2012 in China: 243 women with GDM were matched with 243 non-GDM controls on age (±1 year). Conditional logistic regression and restricted cubic spline were used to examine full-range associations of bile acid metabolites with GDM. All the 9 detectable bile acids were inversely associated with the risk of GDM, among them, 8 in nonlinear and one in largely linear manners in multivariable analysis. Glycoursodeoxycholic acid (GUDCA) at ≤0.07 nmol/mL and deoxycholic acid (DCA) at ≤0.28 nmol/mL had threshold effects and their decreasing levels below the cutoff points were associated with rapid rises in the risk of GDM. In traditional risk factor model, the stepwise procedure identified that GUDCA ≤ 0.07 nmol/mL and DCA ≤ 0.280 nmol/mL were still significant (OR: 6.84, 95%CI: 1.10-42.48 & 2.06, 1.26-3.37), while other bile acids were not. Inclusion of the two bile acids in the model increased the area under operating characteristic's curve from 0.69 to 0.76 (95% CI: 0.71-0.80) (P < .05). Serum GUDCA ≤ 0.07 nmol/mL and DCA ≤ 0.28 nmol/mL in early pregnancy were independently associated with increased risk of GDM in Chinese pregnant women. Talent Recruitment Scheme grant of Tianjin Medical University and National Key Research and Development Program, etc.

Objective: This study aimed to test associations between type 2 diabetes mellitus (T2DM) and metabolites in urea cycle including arginine, citrulline and ornithine. Methods:This study used a hospital-based cross-sectional study design. We retrieved medical notes of 401 in-patients with onset of T2DM within 2 years and 1,522 healthy subjects who attended annual physical examination. All cases were admitted to a tertiary care center in Jinzhou, China from May 2015 to August 2016. Binary logistic regression analyses were performed to obtain odds ratios (ORs) and 95% confidence intervals (CIs). Results:Patients with T2DM had higher arginine, and lower ornithine than control subjects. Levels of citrulline were similar in two groups. Arginine was positively associated with T2DM (ORs: 1.20, 1.17–1.23) while ornithine was negatively associated with T2DM (OR: 0.89, 0.88–0.91). After adjustment for other amino acids and traditional risk factors, these associations were still significant and persistent for arginine and ornithine. The association between citrulline and T2DM was not significant. Their ratios of pairs of two amino acids were associated with increased risk of T2DM. After adjustment for other ratios of amino acids, effect size for T2DM remained significant. Further adjustment for traditional risk factors did not lead to large changes (ORs: 1.78, 1.20–2.65 for the ratio of arginine to ornithine; ORs: 1.59, 1.37–1.86 for the ratio of citrulline to ornithine, respectively) except the ratio of arginine to citrulline. Conclusions: Plasma levels of amino acids related to urea cycle and their ratios of these amino-acids were associated with T2DM in Chinese adults.

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.

Objectives This study aimed to investigate the associations between trimethylamine N-oxide (TMAO) and related metabolites in early pregnancy and the risk of gestational diabetes mellitus (GDM). Design A prospective cohort of 22,302 pregnant women from 2010 to 2012 in Tianjin, China, was used to perform a nested case-control study. A total of 243 women with GDM and 243 women without GDM matched by maternal age (±1 year) were used as cases and controls, respectively. Conditional logistic regression and restricted cubic spline were used to examine the full-range risk associations between individual TMAOs metabolites at the first antenatal care visit with GDM. Trimethylamine conversion ratio (TMA_R) was defined as trimethylamine (TMA)/its precursors, and trimethylamine N-oxide conversion ratio (TMAO_R) was defined as TMAO/TMA. An additive interaction between high TMA_R and low TMAO_R indicates a state of TMA accumulation, and a mathematical interaction between high TMA_R and high TMAO_R indicates accumulation of TMAO. Results TMA was linearly associated with GDM, whereas TMA precursors and TMAO were inversely associated with GDM with clear threshold effects, i.e., 16 nmol/mL for TMAO, 200 nmol/mL for betaine, 112 nmol/mL for l-carnitine, and 110 and 270 nmol/mL for cholinechloride (a U-shaped relationship). Copresence of TMA_R >0.35 and TMAO_R ≤0.15 was associated with a markedly higher OR (11.16; 95% CI, 5.45 to 22.8), compared with TMA_R >0.35 only (OR = 1.71; 95% CI, 0.42 to 6.95) or TMAO_R ≤0.15 only (OR = 2.06; 95% CI, 1.09 to 3.90), with a significant additive interaction. However, the mathematical interaction was nonsignificant. Conclusions TMAO metabolites in the early pregnancy were associated with the risk of GDM, whereas TMA was more likely to play a causal role in GDM.