Association of demographic and clinical factors with risk of acute pancreatitis: An exposure-wide Mendelian randomization study.

BACKGROUND: The incidence of acute pancreatitis (AP) is increasing over years, which brings enormous economy and health burden. However, the aetiologies of AP and underlying mechanisms are still unclear. Here, we performed a two-sample Mendelian randomization (MR) analysis to investigate the associations between all reported possible risk factors and AP using publicly available genome-wide association study summary statistics. METHODS: A series of quality control steps were taken in our analysis to select eligible instrumental single nucleotide polymorphisms which were strongly associated with exposures. To make the conclusions more robust and reliable, we utilized several analytical methods (inverse-variance weighting, MR-PRESSO method, weighted median, MR-Egger regression) that are based on different assumptions of two-sample MR analysis. The MR-Egger intercept test, radial regression and leave-one-out sensitivity analysis were performed to evaluate the horizontal pleiotropy, heterogeneities, and stability of these genetic variants on each exposure. A two-step MR method was applied to explore mediators in significant associations. RESULTS: Genetic predisposition to cholelithiasis (effect estimate: 17.30, 95% CI: 12.25-22.36, p = 1.95 E-11), body mass index (0.32, 95% CI: 0.13-0.51, p < 0.001), body fat percentage (0.57, 95% CI: 0.31-0.83, p = 1.31 E-05), trunk fat percentage (0.36, 95% CI: 0.14-0.59, p < 0.005), ever smoked (1.61, 95% CI: 0.45-2.77, p = 0.007), and limbs fat percentage (0.55, 95% CI: 0.41-0.69, p < 0.001) were associated with an increased risk of AP. In addition, whole-body fat-free mass (-0.32, 95% CI: -0.55 to -0.10, p = 0.004) was associated with a decrease risk of AP. CONCLUSION: Genetic predisposition to cholelithiasis, obesity and smoking could be causally associated with an increased risk of AP, and whole body fat-free mass could be associated with a decreased risk of AP.

New-onset diabetes secondary to acute pancreatitis: An update.

Diabetes is a condition of persistent hyperglycemia caused by the endocrine disorder of the pancreas. Therefore, all pancreatic diseases have the risk of diabetes. In particular, increasing attention has been paid recently to new-onset diabetes secondary to acute pancreatitis (AP). The complications of secondary diabetes have caused a lot of trouble for patients and have garnered increasing attention. At present, the pathophysiological mechanism of new-onset diabetes caused by AP is not clear. This review summarizes the current understanding of new-onset diabetes secondary to AP.

Dose-response relationship between risk factors and incidence of COVID-19 in 325 hospitalized patients: A multicenter retrospective cohort study.

BACKGROUND: The epidemiological and clinical characteristics of coronavirus disease 2019 (COVID-19) patients have been widely reported, but the assessment of dose-response relationships and risk factors for mortality and severe cases and clinical outcomes remain unclear. AIM: To determine the dose-response relationship between risk factors and incidence of COVID-19. METHODS: In this retrospective, multicenter cohort study, we included patients with confirmed COVID-19 infection who had been discharged or had died by February 6, 2020. We used multivariable logistic regression and Cox proportional hazard models to determine the dose-response relationship between risk factors and incidence of COVID-19. RESULTS: It clarified that increasing risk of in-hospital death were associated with older age (HR: 1.04, 95%CI: 1.01-1.09), higher lactate dehydrogenase [HR: 1.04, 95% confidence interval (CI): 1.01-1.10], C-reactive protein (HR: 1.10, 95%CI: 1.01-1.23), and procalcitonin (natural log-transformed HR: 1.88, 95%CI: 1.22-2.88), and D-dimer greater than 1 µg/mL at admission (natural log transformed HR: 1.63, 95%CI: 1.03-2.58) by multivariable regression. D-dimer and procalcitonin were logarithmically correlated with COVID-19 mortality risk, while there was a linear dose-response correlation between age, lactate dehydrogenase, D-dimer and procalcitonin, independent of established risk factors. CONCLUSION: Higher lactate dehydrogenase, D-dimer, and procalcitonin levels were independently associated with a dose-response increased risk of COVID-19 mortality.

Serum magnesium level as a predictor of acute kidney injury in patients with acute pancreatitis.

BACKGROUND: Decreased serum magnesium (Mg2+) is commonly seen in critically ill patients. Hypomagnesemia is significantly more frequent in patients with severe acute pancreatitis. Acute kidney injury (AKI) in patients with acute pancreatitis (AP) is associated with an extremely high mortality. The association underlying serum Mg2+ and AKI in AP has not been elucidated. AIM: To explore the association between serum Mg2+ on admission and AKI in patients with AP. METHODS: A retrospective observational study was conducted in a cohort of patients (n = 233) with AP without any renal injury before admission to our center from August 2015 to February 2019. Demographic characteristics on admission, severity score, laboratory values and in-hospital mortality were compared between patients with and without AKI. RESULTS: A total of 233 patients were included for analysis, including 85 with AKI. Compared to patients without AKI, serum Mg2+ level was significantly lower in patients with AKI at admission [OR = 6.070, 95%CI: 3.374-10.921, P < 0.001]. Multivariate logistic analysis showed that lower serum Mg2+ was an independent risk factor for AKI [OR = 8.47, 95%CI: 3.02-23.72, P < 0.001]. CONCLUSION: Our analysis indicates that serum Mg2+ level at admission is independently associated with the development of AKI in patients with AP and may be a potential prognostic factor.

Machine Learning Models of Acute Kidney Injury Prediction in Acute Pancreatitis Patients.

Background. Acute kidney injury (AKI) has long been recognized as a common and important complication of acute pancreatitis (AP). In the study, machine learning (ML) techniques were used to establish predictive models for AKI in AP patients during hospitalization. This is a retrospective review of prospectively collected data of AP patients admitted within one week after the onset of abdominal pain to our department from January 2014 to January 2019. Eighty patients developed AKI after admission (AKI group) and 254 patients did not (non-AKI group) in the hospital. With the provision of additional information such as demographic characteristics or laboratory data, support vector machine (SVM), random forest (RF), classification and regression tree (CART), and extreme gradient boosting (XGBoost) were used to build models of AKI prediction and compared to the predictive performance of the classic model using logistic regression (LR). XGBoost performed best in predicting AKI with an AUC of 91.93% among the machine learning models. The AUC of logistic regression analysis was 87.28%. Present findings suggest that compared to the classical logistic regression model, machine learning models using features that can be easily obtained at admission had a better performance in predicting AKI in the AP patients.