The visceral adiposity index predicts the severity of hyperlipidaemic acute pancreatitis

Long-chain fatty acids - The turning point between 'mild' and 'severe' acute pancreatitis

Acute pancreatitis (AP) is an inflammatory disease characterized by localized pancreatic injury and a systemic inflammatory response. Fatty acids (FAs), produced during the breakdown of triglycerides (TGs) in blood and peripancreatic fat, escalate local pancreatic inflammation to a systemic level by damaging pancreatic acinar cells (PACs) and triggering M1 macrophage polarization. This paper provides a comprehensive analysis of lipases' roles in the onset and progression of AP, as well as the effects of long-chain fatty acids (LCFAs) on the function of pancreatic acinar cells (PACs). Abnormalities in the function of PACs include Ca2+ overload, premature trypsinogen activation, protein kinase C (PKC) expression, endoplasmic reticulum (ER) stress, and mitochondrial and autophagic dysfunction. The study highlights the contribution of long-chain saturated fatty acids (LC-SFAs), especially palmitic acid (PA), to M1 macrophage polarization through the activation of the NLRP3 inflammasome and the NF-κB pathway. Furthermore, we investigated lipid lowering therapy for AP. This review establishes a theoretical foundation for pro-inflammatory mechanisms associated with FAs in AP and facilitating drug development.

Machine learning improves early prediction of organ failure in hyperlipidemia acute pancreatitis using clinical and abdominal CT features

BACKGROUND

This study aimed to investigate and validate machine-learning predictive models combining computed tomography and clinical data to early predict organ failure (OF) in Hyperlipidemic acute pancreatitis (HLAP).

METHODS

Demographics, laboratory parameters and computed tomography imaging data of 314 patients with HLAP from the First Affiliated Hospital of Wenzhou Medical University between 2017 and 2021, were retrospectively analyzed. Sixty-five percent of patients (n = 204) were assigned to the training group and categorized as patients with and without OF. Parameters were compared by univariate analysis. Machine-learning methods including random forest (RF) were used to establish model to predict OF of HLAP. Areas under the curves (AUCs) of receiver operating characteristic were calculated. The remaining 35% patients (n = 110) were assigned to the validation group to evaluate the performance of models to predict OF.

RESULTS

Ninety-three (45.59%) and fifty (45.45%) patients from the training and the validation cohort, respectively, developed OF. The RF model showed the best performance to predict OF, with the highest AUC value of 0.915. The sensitivity (0.828) and accuracy (0.814) of RF model were both the highest among the five models in the study cohort. In the validation cohort, RF model continued to show the highest AUC (0.820), accuracy (0.773) and sensitivity (0.800) to predict OF in HLAP, while the positive and negative likelihood ratios and post-test probability were 3.22, 0.267 and 72.85%, respectively.

CONCLUSIONS

Machine-learning models can be used to predict OF occurrence in HLAP in our pilot study. RF model showed the best predictive performance, which may be a promising candidate for further clinical validation.

Novel anthropometric indicators of visceral obesity predict the severity of hyperlipidemic acute pancreatitis

BACKGROUND

Obesity substantially contributes to the onset of acute pancreatitis (AP) and influences its progression to severe AP. Although body mass index (BMI) is a widely used anthropometric parameter, it fails to delineate the distribution pattern of adipose tissue. To circumvent this shortcoming, the predictive efficacies of novel anthropometric indicators of visceral obesity, such as lipid accumulation products (LAP), cardiometabolic index (CMI), body roundness index (BRI), visceral adiposity index (VAI), A Body Shape Index (ABSI), and Chinese visceral adiposity index (CVAI) were examined to assess the severity of AP.

METHOD

The body parameters and laboratory indices of 283 patients with hyperlipidemic acute pancreatitis (HLAP) were retrospectively analysed, and the six novel anthropometric indicators of visceral obesity were calculated. The severity of HLAP was determined using the revised Atlanta classification. The correlation between the six indicators and HLAP severity was evaluated, and the predictive efficacy of the indicators was assessed using area under the curve (AUC). The differences in diagnostic values of the six indicators were also compared using the DeLong test.

RESULTS

Patients with moderate to severe AP had higher VAI, CMI, and LAP than patients with mild AP (all P < 0.001). The highest AUC in predicting HLAP severity was observed for VAI, with a value of 0.733 and 95% confidence interval of 0.678-0.784.

CONCLUSIONS

This study demonstrated significant correlations between HLAP severity and VAI, CMI, and LAP indicators. These indicators, particularly VAI, which displayed the highest predictive power, were instrumental in forecasting and evaluating the severity of HLAP.

Predictive value of adipokines for the severity of acute pancreatitis: a meta-analysis

BACKGROUND

Severe acute pancreatitis (SAP) is a dangerous condition with a high mortality rate. Many studies have found an association between adipokines and the development of SAP, but the results are controversial. Therefore, we performed a meta-analysis of the association of inflammatory adipokines with SAP.

METHODS

We screened PubMed, EMBASE, Web of Science and Cochrane Library for articles on adipokines and SAP published before July 20, 2023. The quality of the literature was assessed using QUADAS criteria. Standardized mean differences (SMD) with 95% confidence intervals (CI) were calculated to assess the combined effect. Subgroup analysis, sensitivity analysis and publication bias tests were also performed on the information obtained.

RESULT

Fifteen eligible studies included 1332 patients with acute pancreatitis (AP). Pooled analysis showed that patients with SAP had significantly higher serum levels of resistin (SMD = 0.78, 95% CI:0.37 to 1.19, z = 3.75, P = 0.000). The difference in leptin and adiponectin levels between SAP and mild acute pancreatitis (MAP) patients were not significant (SMD = 0.30, 95% CI: -0.08 to 0.68, z = 1.53, P = 0.127 and SMD = 0.11, 95% CI: -0.17 to 0.40, z = 0.80, P = 0.425, respectively). In patients with SAP, visfatin levels were not significantly different from that in patients with MAP (SMD = 1.20, 95% CI: -0.48 to 2.88, z = 1.40, P = 0.162).

CONCLUSION

Elevated levels of resistin are associated with the development of SAP. Resistin may serve as biomarker for SAP and has promise as therapeutic target.

The weight-adjusted-waist index predicts all-cause and cardiovascular mortality in general US adults

BACKGROUND

The authors examined the relationship between Weight-adjusted Waist Index (WWI) and all-cause and cardiovascular mortality among adults in the US.

METHODS

This prospective cohort study included 26,882 individuals who participated in the National Health and Nutrition Examination Survey (NHANES) from 2005 through 2014. WWI was calculated as waist circumference divided by the square root of weight. The main outcomes of this study were all-cause mortality and cardiovascular mortality. Mortality status and cause of death were determined by NHANES-linked National Death Index records through December 31, 2015. Cox proportional hazard models and Kaplan-Meier analysis were used to estimate Hazard Ratios (HR) and 95% CIs for mortality for all causes and cardiovascular diseases.

RESULTS

A total of 26,882 participants with a mean WWI of 10.89 ± 0.01, of whom 49.23% were male. The average follow-up time was 68.95 ± 1.07 months, and 1870 participants were determined as deceased (4.99%), including 349 cardiovascular death (0.88%). The Kaplan-Meier analysis demonstrated a significant difference in all-cause and cardiovascular mortality between patients with WWI <11.33 and ≥11.33 (both log-rank test p < 0.0001). The fully adjusted Cox proportional hazard model indicated that a higher WWI level (≥ 11.33) was associated with an increased 95% risk for cardiovascular mortality (HR = 1.95, 95% CI 1.30‒2.93) and 68% risk for all-cause death (HR = 1.68, 95% CI 1.41‒2.00) compared with the counterparts.

CONCLUSIONS

Elevated WWI levels were associated with a higher risk of cardiovascular mortality and all-cause mortality independently.

A cross-sectional study on the relationship between visceral adiposity index and periodontitis in different age groups

Obesity and periodontitis are significantly related, and the visceral adiposity index (VAI) is an important indicator of obesity. This study aimed to investigate the association between VAI and periodontitis. The study included participants from the 2009-2014 cycle of the National Health and Nutrition Examination Survey who received a complete periodontal exam and VAI record. Periodontitis, according to the Centers for Disease Control and Prevention-American Academy of Periodontology periodontitis case definitions, is categorized into the following: no periodontitis, moderate periodontitis, mild periodontitis, and severe periodontitis. Hierarchical analysis, multivariable logistic regression, and restricted cubic spline regression were conducted to investigate the relationship between periodontitis and VAI in adults. There was no significant relationship between VAI and the prevalence of periodontitis in all age groups (P = 0.08). Age-stratified analysis showed a significant association between periodontitis and VAI in adults aged 40-50 years (P < 0.001). After adjusting for all covariates, the association between periodontitis and VAI remained significant in the 40-50-years age group (the trend P value = 0.014). Restricted cubic spline analysis showed a non-linear relationship between VAI and periodontitis (P for non-linear = 0.002). Visceral adiposity index was significantly associated with periodontitis risk in the 40-50-year-old group, and the relationship between VAI and periodontitis risk was found to be non-linear.