The gut microbiome and microbial metabolites in acute myocardial infarction.

Emerging evidence has highlighted the role of gut microbiome in human health. However, the integrative role of gut microbiome and microbial metabolites in acute myocardial infarction (AMI) remains unclear. The current study profiles the microbial community through 16S rRNA gene sequencing and shotgun metagenomic sequencing and measures fecal short-chain fatty acids and circulating choline pathway metabolites among 117 new-onset AMI cases and 78 controls. Significant microbial alternations are observed in AMI patients compared with controls (P = 0.001). The abundances of nine species (e.g., Streptococcus salivarius and Klebsiella pneumoniae) are positively associated, and one species (Roseburia hominis) is inversely associated with AMI status and severity. A gut microbial score at disease onset is associated with the risk of major adverse cardiovascular events in 3.2 years (hazard ratio [95% CI]: 2.01 [1.04-4.24]) in AMI patients. The molar proportions of fecal acetate and butyrate are higher, and the circulating levels of choline and carnitine are lower in AMI patients than in controls. In addition, disease classifiers show that AMI cases and controls have a more distinct pattern in taxonomical composition than in pathways or metabolites. Our findings suggest that microbial composition and functional potentials are associated with AMI status and severity.

A Predictive Model Based on a New CI-AKI Definition to Predict Contrast Induced Nephropathy in Patients With Coronary Artery Disease With Relatively Normal Renal Function.

Background: Contrast induced nephropathy (CIN) is a common complication in patients receiving intravascular contrast media. In 2020, the American College of Radiology and the National Kidney Foundation issued a new contrast induced acute kidney injury (CI-AKI) criteria. Therefore, we aimed to explore the potential risk factors for CIN under the new criteria, and develop a predictive model for patients with coronary artery disease (CAD) with relatively normal renal function (NRF). Methods: Patients undergoing coronary angiography or percutaneous coronary intervention at Zhongshan Hospital, Fudan University between May 2019 and April 2020 were consecutively enrolled. Eligible candidates were selected for statistical analysis. Univariate and multivariate logistic regression analyses were used to identify the predictive factors. A stepwise method and a machine learning (ML) method were used to construct a model based on the Akaike information criterion. The performance of our model was evaluated using the area under the receiver operating characteristic curves (AUC) and calibration curves. The model was further simplified into a risk score. Results: A total of 2,009 patients with complete information were included in the final statistical analysis. The results showed that the incidence of CIN was 3.2 and 1.2% under the old and new criteria, respectively. Three independent predictors were identified: baseline uric acid level, creatine kinase-MB level, and log (N-terminal pro-brain natriuretic peptide) level. Our stepwise model had an AUC of 0.816, which was higher than that of the ML model (AUC = 0.668, P = 0.09). The model also achieved accurate predictions regarding calibration. A risk score was then developed, and patients were divided into two risk groups: low risk (total score < 10) and high risk (total score ≥ 10). Conclusions: In this study, we first identified important predictors of CIN in patients with CAD with NRF. We then developed the first CI-AKI model on the basis of the new criteria, which exhibited accurate predictive performance. The simplified risk score may be useful in clinical practice to identify high-risk patients.

A novel nomogram based on cardia invasion and chemotherapy to predict postoperative overall survival of gastric cancer patients.

BACKGROUND: We aimed to establish and externally validate a nomogram to predict the 3- and 5-year overall survival (OS) of gastric cancer (GC) patients after surgical resection. METHODS: A total of 6543 patients diagnosed with primary GC during 2004-2016 were collected from the Surveillance, Epidemiology, and End Results (SEER) database. We grouped patients diagnosed during 2004-2012 into a training set (n = 4528) and those diagnosed during 2013-2016 into an external validation set (n = 2015). A nomogram was constructed after univariate and multivariate analysis. Performance was evaluated by Harrell's C-index, area under the receiver operating characteristic curve (AUC), decision curve analysis (DCA), and calibration plot. RESULTS: The multivariate analysis identified age, race, location, tumor size, T stage, N stage, M stage, and chemotherapy as independent prognostic factors. In multivariate analysis, the hazard ratio (HR) of non-cardia invasion was 0.762 (P < 0.001) and that of chemotherapy was 0.556 (P < 0.001). Our nomogram was found to exhibit excellent discrimination: in the training set, Harrell's C-index was superior to that of the 8th American Joint Committee on Cancer (AJCC) TNM classification (0.736 vs 0.699, P < 0.001); the C-index was also better in the validation set (0.748 vs 0.707, P < 0.001). The AUCs for 3- and 5-year OS were 0.806 and 0.815 in the training set and 0.775 and 0.783 in the validation set, respectively. The DCA and calibration plot of the model also shows good performance. CONCLUSIONS: We established a well-designed nomogram to accurately predict the OS of primary GC patients after surgical resection. We also further confirmed the prognostic value of cardia invasion and chemotherapy in predicting the survival rate of GC patients.