Atherogenic Index of Plasma Is a Potential Biomarker for Severe Acute Pancreatitis: A Prospective Observational Study

Diabetes Mellitus Mediates the Relationship Between Atherogenic Index of Plasma and Gallstones: A Population-Based Cross-Sectional Study

Purpose

Previous studies have shown a correlation between diabetes mellitus and gallstone formation. The atherogenic index of plasma (AIP) is associated with many metabolic diseases. However, insufficient evidence still exists to elucidate the association between AIP and gallstones. The primary objective of this study was to investigate the correlation between AIP and gallstones in US adults, and the secondary objective was to analyze whether diabetes plays a mediating role in the association.

Patients and Methods

Using data from the National Health and Nutrition Survey (NHANES) conducted between 2017 and March 2020, this study investigated the association between AIP and gallstone incidence in US adults. A variety of statistical methods were used to analyze the data in this study, including multivariate logistic regression, subgroup analyses, restricted cubic spline curves (RCS), and mediation effects analysis. In addition, two-stage linear regression was used to detect possible threshold and saturation effects.

Results

A total of 6952 subjects were enrolled in the trial, of which 748 patients were diagnosed with gallstones. A significant positive association between AIP and gallstones was observed by fully adjusted multivariate logistic regression analysis, with an odds ratio (OR) of 1.45 and a 95% confidence interval (CI) of (1.09, 1.93). In addition, a non-linear positive association and saturation effect between AIP and gallstones were found, with an inflection point of 0.2246. Mediation analysis showed that diabetes had a mediating effect of 16.9% in the association between AIP and gallstones.

Conclusion

This study suggests that elevated levels of AIP are linked to an augmented vulnerability to gallstone development, with diabetes serving as a mediating factor. These findings present a novel perspective on clinical approaches to prevent and manage gallstones.

Decreased HDL-C Levels as a Predictor of Organ Failure in Acute Pancreatitis in the Emergency Department

High-density lipoprotein cholesterol (HDL-C) is reported as a biomarker of systemic inflammation and multi-organ failure (MOF), which has been rarely investigated in acute pancreatitis (AP), a frequent condition in the emergency department (ED). The objective was to study the predictive capacity of the decrease in HDL-C to the progression of MOF in AP in the ED; analyzing 114 patients with AP for one year in a longitudinal and prospective study, AP severity was obtained by the Atlanta classification, in relation to modified Marshall and Bedside Index for Severity in Acute Pancreatitis (BISAP) scores, and clinical and laboratory parameters in a 48 h hospital stay. The area under the receiver operating characteristic (ROC) curve was used to estimate the validity of the predictor and define optimal cut-off points. It was found that AP was classified as severe in 24.5%, mainly for biliary etiology (78.9%) and female sex (73.6%). As a biomarker, HDL-C decreased from 31.6 to 29.5 mg/dL in a 48 h stay (p < 0.001), correlating negatively with the increase in severity index > 2 and the modified Marshall (p < 0.032) and BISAP (p < 0.009) scores, finding an area under the ROC curve with a predictive capacity of 0.756 (95% CI, 0.614-0.898; p < 0.004) and a cut-off point of 28.5 mg/dL (sensitivity: 79%, specificity: 78%), demonstrating that the decrease in HDL-C levels serves as a useful indicator with a predictive capacity for MOF in mild to severe AP, during a 48 h hospital stay in the ED.

Early prediction of acute pancreatitis severity based on changes in pancreatic and peripancreatic computed tomography radiomics nomogram

Background

Early identification of severe acute pancreatitis (SAP) is key to reducing mortality and improving prognosis. We aimed to establish a radiomics model and nomogram for early prediction of acute pancreatitis (AP) severity based on contrast-enhanced computed tomography (CT) images.

Methods

We retrospectively analyzed 215 patients with first-episode AP, including 141 in the training cohort (87 men and 54 women, mean age 51.37±16.09 years) and 74 in the test cohort (40 men and 34 women, mean age 55.49±17.83 years). Radiomics features were extracted from portal venous phase images based on pancreatic and peripancreatic regions. The light gradient boosting machine (LightGBM) algorithm was used for feature selection, a logistic regression (LR) model was established and trained by 10-fold cross-validation, and a nomogram was established based on the best features. The model's predictive performance was evaluated according to the area under the curve (AUC) of the receiver operating characteristic (ROC) curve, sensitivity, specificity, and accuracy.

Results

A total of 13 optimal radiomics features were selected by LightGBM for LR model building. The AUC of the radiomics (LR) model was 0.992 [95% confidence interval (CI): 0.963-0.996] in the training cohort, 0.965 (95% CI: 0.924-0.981) in the validation cohort, and 0.894 (95% CI: 0.789-0.966) in the test cohort. The sensitivity was 0.862 (95% CI: 0.674-0.954), the specificity was 0.800 (95% CI: 0.649-0.899), and the accuracy was 0.824 (95% CI: 0.720-0.919). The nomogram based on the 13 radiomics features showed that SAP would be predicted when the total score was greater than 124.

Conclusions

The radiomics model based on enhanced-CT images of pancreatic and peripancreatic regions performed well in the early prediction of AP severity. The nomogram based on selected radiomics features could provide a reference for AP clinical assessment.

Mendelian randomization in blood metabolites identifies triglycerides and fatty acids saturation level as associated traits linked to pancreatitis risk

Background

There is very limited evidence on the causal effects of blood metabolites on pancreatitis risks. To reveal the causal associations between plasma metabolites and pancreatitis risks, we performed two-sample Mendelian randomization (MR) and Bayesian model averaging (MR-BMA) analyses in European ancestry.

Methods

The summary-level statistics from two genome-wide association studies with 249 and 123 metabolic traits derived from two separate cohorts involving ~115,000 (UK Biobank) and ~25,000 individuals from European ancestry were used for the analyses. The summary statistics of four pancreatitis datasets from FinnGen R5 and two pancreatitis datasets from UK Biobank were exploited as the outcome. We first performed univariable MR analysis with different metabolic GWAS data on multiple pancreatitis datasets to demonstrate the association pattern among different metabolites categories. Next, we exploited the MR-BMA method to pinpoint the dominating factors on the increased risk of pancreatitis.

Results

In the primary analysis with 249 traits, we found that plasma triglycerides were positively associated with pancreatitis risk. Intriguingly, a large number of traits associated with saturation or unsaturation of fatty acids also demonstrated causal associations. The replication study analyzing 123 metabolic traits suggested that bisallylic groups levels and omega-3 fatty acids were inversely correlated with pancreatitis risk. MR-BMA analyses indicated that the ratio of triglycerides to total lipid in various HDL particles played leading roles in pancreatitis susceptibility. In addition, the degree of unsaturation, the ratio of polyunsaturated fatty acids to monounsaturated fatty acids and the level of monounsaturated fatty acids showed causal associations with either decreased or increased pancreatitis susceptibility.

Conclusions

Our MR study provided an atlas of causal associations of genetically predicted blood metabolites on pancreatitis, and offered genetic insights showing intervention in triglycerides and the supplementation of unsaturated fatty acids are potential strategies in the primary prevention of pancreatitis.

Dysregulated Serum Lipid Metabolism Promotes the Occurrence and Development of Diabetic Retinopathy Associated With Upregulated Circulating Levels of VEGF-A, VEGF-D, and PlGF

Purpose: This study aims to explore the correlations of arteriosclerosis-associated plasma indices with various severity levels of diabetic retinopathy (DR) and to test the hypothesis that elevated circulating level of known angiogenic cytokines induced by hyperglycemia is associated with dyslipidemia on DR.

Methods: This cross-sectional study consists of 131 patients with type 2 diabetes. The patients were categorized based on their DR status into those with no DR (diabetes mellitus, DM), non-proliferative diabetic retinopathy (NPDR), and proliferative diabetic retinopathy (PDR) groups. The biochemical profile including fasting glucose, glycated hemoglobin (HbA1c), lipid profile were estimated, plasma angiogenic cytokines (vascular endothelial growth factor, VEGF-A, -C, -D) and placental growth factor (PlGF) were analyzed by protein microarrays. The atherogenic plasma index (API) was defined as low-density lipoprotein cholesterol/high-density lipoprotein cholesterol (LDL-C/HDL-C); atherogenic index (AI) was calculated as (TC-(HDL-C))/HDL-C and atherogenic index of plasma (AIP) was defined as log (TG/HDL-C).

Results: No significant differences were detected in the duration of hypertension, age, and gender between the three groups. Serum TC and LDL-C, AI, and API in the NPDR group and PDR group were significantly higher than those in the DM group. The circulating level of PlGF, VEGF-A, and VEGF-C were significantly correlated with the severity of DR. VEGF-D is a risk factor independent of API (Z = −2.61, P = 0.009) and AI (Z = −2.40, P = 0.016). Multivariate logistic regression showed that AI and API are strong risk factors for the occurrence and severity of DR. Associated with AI and API, VEGF-D and PlGF contribute to DR: VEGF-D [AI: P = 0.038, odd ratio (OR) = 1.38; VEGF-D: P = 0.002, OR = 1.00. API: P = 0.027, OR = 1.56, VEGF-D:P = 0.002, OR = 1.00] and PlGF [AI: P = 0.021, OR = 1.43; VEGF-D: P = 0.004, OR = 1.50. API: P = 0.011, OR = 1.66; VEGF-D: P = 0.005, OR = 1.49].

Conclusions: Total cholesterol (TC) and LDL-C are risk factors for presence of any DR. Atherogenic index and API are novel and better predictive indicators for the occurrence and severity of DR in comparion with the traditional lipid profiles. Abnormal lipid metabolism are associated with the upregulation of circulating cytokines that are linked to the severity of DR.

Comparison of MPL-ANN and PLS-DA models for predicting the severity of patients with acute pancreatitis: An exploratory study

OBJECTIVE

Acute pancreatitis (AP) is a common inflammatory disorder that may develop into severe AP (SAP), resulting in life-threatening complications and even death. The purpose of this study was to explore two different machine learning models of multilayer perception-artificial neural network (MPL-ANN) and partial least squares-discrimination (PLS-DA) to diagnose and predict AP patients' severity.

METHODS

The MPL-ANN and PLS-DA models were established using candidate markers from 15 blood routine parameters and five serum biochemical indexes of 133 mild acute pancreatitis (MAP) patients, 167 SAP (including 88 moderately SAP) patients, and 69 healthy controls (HCs). The independent parameters and combined model's diagnostic efficiency in AP severity differentiation were analyzed using the area under the receiver operating characteristic curve (AUC).

RESULTS

The neutrophil to lymphocyte ratio (NLR) is the most useful marker in 20 parameters for screening AP patients [AUC = 0.990, 95% confidence interval (CI): 0.984-0.997, sensitivity 94.3%, specificity 98.6%]. The MPL-ANN model based on six optimal parameters exhibited better diagnostic and predict performance (AUC = 0.984, 95% CI: 0.960-1.00, sensitivity 92.7%, specificity 93.3%, accuracy 93.0%) than the PLS-DA model based on five optimal parameters (AUC = 0.912, 95% CI: 0.853-0.971, sensitivity 87.8%, specificity 84.4%, accuracy 84.8%) in discriminating MAP patients from SAP patients.

CONCLUSION

The results demonstrated that the MPL-ANN model based on routine blood and serum biochemical indexes provides a reliable and straightforward daily clinical practice tool to predict AP patients' severity.