Association Between Metabolic Syndrome and Its Components with Severity of Acute Pancreatitis

Identification of shared genetic risks underlying metabolic syndrome and its related traits in the Korean population

Introduction: Observational studies have demonstrated strong correlations between metabolic syndrome (MetS) and its related traits. To gain insight into the genetic architecture and molecular mechanism of MetS, we investigated the shared genetic basis of MetS and its related traits and further tested their causal relationships. Methods: Using summary statistics from genome-wide association analyses of about 72,000 subjects from the Korean Genome and Epidemiological Study (KoGES), we conducted genome-wide multi-trait analyses to quantify the overall genetic correlation and Mendelian randomization analyses to infer the causal relationships between traits of interest. Results: Genetic correlation analyses revealed a significant correlation of MetS with its related traits, such as obesity traits (body mass index and waist circumference), lipid traits (triglyceride and high-density lipoprotein cholesterol), glycemic traits (fasting plasma glucose and hemoglobin A1C), and blood pressure (systolic and diastolic). Mendelian randomization analyses further demonstrated that the MetS-related traits showing significant overall genetic correlation with MetS could be genetically determined risk factors for MetS. Discussion: Our study suggests a shared genetic basis of MetS and its related traits and provides novel insights into the biological mechanisms underlying these complex traits. Our findings further inform public health interventions by supporting the important role of the management of metabolic risk factors such as obesity, unhealthy lipid profiles, diabetes, and high blood pressure in the prevention of MetS.

Acute pancreatitis and metabolic syndrome: genetic correlations and causal associations

BACKGROUND

Although there is a definite correlation between the Metabolic Syndrome (MetS) and Acute Pancreatitis (AP), cause is yet unknown. The current work combined linkage disequilibrium score (LDSC) regression and Mendelian randomization (MR) approaches to fill this important information gap.

METHODS

In this study, we harnessed the power of publicly available gene-wide association databases (GWAS) to explore the intricate relationship between MetS and its components with AP. The cornerstone of our analysis was the Inverse-Variance Weighted (IVW) method, serving as our primary analytical tool. In addition to IVW, we complemented our investigation with several other robust MR methods, including MR-Egger, Weighted Median, Maximum Likelihood, and MR-PRESSO. By employing this diverse set of analytical approaches, we sought to ensure the comprehensiveness and robustness of our findings.

RESULT

LDSC regression indicated a genetic correlation between MetS and AP. Univariate MR results indicated a genetic association between MetS (OR = 1.084; 95% CI, 1.005-1.170; P = 0.037), BMI (OR = 1.459; 95% CI, 1.325-1.606; P = 1.46E-14), WHR (OR = 1.189; 95% CI, 1.068-1.323; P = 1.56 E-03), TG (OR = 1.110; 95% CI, 1.001-1.231; P = 0.047), and FI (OR = 1.798; 95% CI, 1.245-2.595; P = 1.74E-03) were able to significantly increase the risk of AP. The results of multivariate MR analysis revealed that these causality associations still existed.

CONCLUSION

Our investigation has yielded compelling evidence that substantiates the presence of both a genetic correlation and a causal relationship between MetS and AP.

2024 Heart Disease and Stroke Statistics: A Report of US and Global Data From the American Heart Association

BACKGROUND

The American Heart Association (AHA), in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, nutrition, sleep, and obesity) and health factors (cholesterol, blood pressure, glucose control, and metabolic syndrome) that contribute to cardiovascular health. The AHA Heart Disease and Stroke Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, brain health, complications of pregnancy, kidney disease, congenital heart disease, rhythm disorders, sudden cardiac arrest, subclinical atherosclerosis, coronary heart disease, cardiomyopathy, heart failure, valvular disease, venous thromboembolism, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs).

METHODS

The AHA, through its Epidemiology and Prevention Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States and globally to provide the most current information available in the annual Statistical Update with review of published literature through the year before writing. The 2024 AHA Statistical Update is the product of a full year's worth of effort in 2023 by dedicated volunteer clinicians and scientists, committed government professionals, and AHA staff members. The AHA strives to further understand and help heal health problems inflicted by structural racism, a public health crisis that can significantly damage physical and mental health and perpetuate disparities in access to health care, education, income, housing, and several other factors vital to healthy lives. This year's edition includes additional global data, as well as data on the monitoring and benefits of cardiovascular health in the population, with an enhanced focus on health equity across several key domains.

RESULTS

Each of the chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics.

CONCLUSIONS

The Statistical Update represents a critical resource for the lay public, policymakers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.

Influence of enlarged waist circumference and hypertriglyceridemia in the severity of acute pancreatitis: A retrospective study

INTRODUCTION

Acute pancreatitis is a frequent inflammatory gastrointestinal disorder with high mortality rates in severe forms. An early evaluation of its severity is key to identify high-risk patients. This study assessed the influence of waist circumference together with hypertriglyceridemia on the severity of acute pancreatitis.

METHODS

A retrospective study was performed, which included patients admitted with acute pancreatitis from March 2014 to March 2021. Patients were classified into four phenotype groups according to their waist circumference and triglyceride levels: normal waist circumference and normal triglycerides; normal waist circumference and elevated triglycerides; enlarged waist circumference and normal triglycerides; and enlarged waist circumference and triglycerides, namely hypertriglyceridemic waist (HTGW) phenotype. Clinical outcomes were compared among the groups.

RESULTS

407 patients were included. Systemic inflammatory response syndrome (SIRS) and intensive care unit admission were most frequent among patients in the HTGW phenotype group, at 44.9% and 8.2%, respectively. The incidence of local complications was higher in the normal waist circumference with elevated triglycerides group (27%). On multivariable analysis, an enlarged waist circumference was related to an increase of 4% and 2% in the likelihood of developing organ failure and SIRS, respectively. Hypertriglyceridemia was an independent risk factor for both organ failure and local complications.

CONCLUSIONS

HTGW phenotype was significant related to developing of SIRS. It seems that an enlarged waist circumference has a greater role than hypertriglyceridemia in the development of SIRS. Obesity and hypertriglyceridemia were both independent risk factors for organ failure. Patients with hypertriglyceridemia were more likely to develop local complications.

High Triglyceride-Glucose Index Is Associated with Poor Prognosis in Patients with Acute Pancreatitis

BACKGROUND

Acute pancreatitis (AP) is a common gastrointestinal disease worldwide. Severe acute pancreatitis (SAP) is characterized as persistent organ failure with a mortality rate as high as 20-30%. Early assessment of the severity and screening out possible SAP is of great significance. Given that there is still a lack of both convenient and practical tools for evaluating SAP, we conducted this study to explore the association between TyG index and acute pancreatitis prognosis.

METHODS

A total of 353 in-patients diagnosed with acute pancreatitis in the Second Hospital of Shandong University were retrospectively enrolled from January 2018 to November 2021 in this study. According to the Atlanta Classification, they were divided into two groups based on the AP severity. Demographic information and clinical materials were retrospectively collected. The TyG index calculation formula is as follows: ln [fasting triglycerides (mg/dL) × fasting plasma glucose (mg/dL)/2]. Statistical analyses were performed using SPSS software (IBM version 22.0) and Medcalc software. Multivariable logistic regression analyses were used to investigate independent predictors for SAP. ROC curve was plotted to assess the predictive ability and cutoffs of TyG index.

RESULTS

A total of 353 AP patients were respectively enrolled in this study, of which 47 suffered from SAP. Compared with the non-SAP group, TyG index was significantly higher in the SAP group (10.44 ± 1.55 vs 9.33 ± 1.44, P < 0.001). Multivariate logistic regression analysis showed that TyG index was an independent risk factor for SAP (OR 1.835, 95% CI 1.380-2.442 P < 0.001), with a cutoff of 8.76 for non-HTG/AAP and 11.81 for HTG/AAP by ROC curve. TyG index of patients who suffered from SIRS, OF, APFC, and ANC was higher than those without (P < 0.05).

CONCLUSIONS

The triglyceride-glucose index is an independent risk factor for SAP. High TyG index is closely related to SAP and AP-related complications.

Heart Disease and Stroke Statistics-2023 Update: A Report From the American Heart Association

BACKGROUND

The American Heart Association, in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, diet, and weight) and health factors (cholesterol, blood pressure, and glucose control) that contribute to cardiovascular health. The Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, congenital heart disease, rhythm disorders, subclinical atherosclerosis, coronary heart disease, heart failure, valvular disease, venous disease, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs).

METHODS

The American Heart Association, through its Epidemiology and Prevention Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States to provide the most current information available in the annual Statistical Update with review of published literature through the year before writing. The 2023 Statistical Update is the product of a full year's worth of effort in 2022 by dedicated volunteer clinicians and scientists, committed government professionals, and American Heart Association staff members. The American Heart Association strives to further understand and help heal health problems inflicted by structural racism, a public health crisis that can significantly damage physical and mental health and perpetuate disparities in access to health care, education, income, housing, and several other factors vital to healthy lives. This year's edition includes additional COVID-19 (coronavirus disease 2019) publications, as well as data on the monitoring and benefits of cardiovascular health in the population, with an enhanced focus on health equity across several key domains.

RESULTS

Each of the chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics.

CONCLUSIONS

The Statistical Update represents a critical resource for the lay public, policymakers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.

Elevated serum HbA1c level, rather than previous history of diabetes, predicts the disease severity and clinical outcomes of acute pancreatitis

INTRODUCTION

The aim of our study is to explore the value of serum glycosylated hemoglobin A1c (HbA1c) in disease severity and clinical outcomes of acute pancreatitis (AP).

RESEARCH DESIGN AND METHODS

Patients with AP were included from January 2013 to December 2020, retrospectively, dividing into normal serum HbA1c level (N-HbA1c) group and high serum HbA1c level (H-HbA1c) group according to the criteria HbA1c <6.5%. We compared patient characteristics, biochemical parameters, disease severity, and clinical outcomes of patients with AP in two groups. Besides, we evaluated the efficacy of serum HbA1c to predict organ failure (OF) in AP patients by receiver operating curve (ROC).

RESULTS

We included 441 patients with AP, including 247 patients in N-HbA1c group and 194 patients in H-HbA1c group. Serum HbA1c level was positively correlated with Atlanta classification, systemic inflammatory response syndrome, local complication, and OF (all p<0.05). Ranson, BISAP (bedside index of severity in acute pancreatitis), and CT severity index scores in patients with H-HbA1c were markedly higher than those in patients with N-HbA1c (all p<0.01). ROC showed that the best critical point for predicting the development of OF in AP with serum HbA1c is 7.05% (area under the ROC curve=0.79). Logistic regression analysis showed H-HbA1c was the independent risk factor for the development of OF in AP. Interestingly, in patients with presence history of diabetes and HbA1c <6.5%, the severity of AP was significantly lower than that in H-HbA1c group. Besides, there was no significant difference between with and without history of diabetes in N-HbA1c group.

CONCLUSIONS

Generally known, diabetes is closely related to the development of AP, and strict control of blood glucose can improve the related complications. Thus, the level of glycemic control before the onset of AP (HbA1c as an indicator) is the key to poor prognosis of AP, rather than basic history of diabetes. Elevated serum HbA1c level can become the potential indicator for predicting the disease severity of AP.

Health-related quality of life among acute pancreatitis patients correlates with metabolic variables and associated factors

Introduction

Acute pancreatitis (AP) and associated metabolic abnormalities constitute prevalent medical disorders that have disastrous implications and expensive cost of care. However, the connection with metabolic abnormalities and their influence on wellbeing i.e., health-related quality of life (HRQoL) remains unclear. As a result, we investigated the influence of MetS components on HRQoL in AP patients.

Methods

In a tertiary care hospital in North India, comprehensive observational research was undertaken with enrollment of subjects having AP along metabolic syndrome (MetS) or without was included. MetS was diagnosed for subjects using the National Cholesterol Education Program–Adult Treatment Panel III (NCEP-ATP III) guidelines. Various socio-demographic variables were also taken into consideration for the calculation of statistical significance (P ≤ 0.05) in AP patients. Student's t-test and Short Form-36 (SF-36) along with the association between AP and MetS, as well as their impact on HRQoL, was investigated finally with, Pearson Correlation Analysis Factor.

Results

The study comprised 100 subjects or patients diseased of AP associated with MetS and 100 patients with AP associated without MetS. Gender, Age, Educational Status, Tobacco uses along with the metabolic variables were found to be statistically significant (P ≤ 0.05) and comparatively increased in patients with AP with MetSthan AP without MetS except HDL levels. Finally, a negative association between all metabolic variables with the exception of HDL, and AP was found to be producing deterioration in Health compartment scores.

Conclusion

AP with MetS patients had a worse aggregate HRQOL than AP without MetS patients.

•

Acute pancreatitis (AP) and associated metabolic abnormalities constitute prevalent medical disorders.

•

We investigated the influence of MetS components on HRQoL in AP patients.

•

The focus of this study is to figure out the relationship between MetS with quality of life among AP patients.

•

AP with MetS patients had a worse aggregate HRQOL than AP without MetS patients.

Development and validation of a risk prediction score for the severity of acute hypertriglyceridemic pancreatitis in Chinese patients

BACKGROUND

The frequency of acute hypertriglyceridemic pancreatitis (AHTGP) is increasing worldwide. AHTGP may be associated with a more severe clinical course and greater mortality than pancreatitis caused by other causes. Early identification of patients with severe inclination is essential for clinical decision-making and improving prognosis. Therefore, we first developed and validated a risk prediction score for the severity of AHTGP in Chinese patients.

AIM

To develop and validate a risk prediction score for the severity of AHTGP in Chinese patients.

METHODS

We performed a retrospective study including 243 patients with AHTGP. Patients were randomly divided into a development cohort (n = 170) and a validation cohort (n = 73). Least absolute shrinkage and selection operator and logistic regression were used to screen 42 potential predictive variables to construct a risk score for the severity of AHTGP. We evaluated the performance of the nomogram and compared it with existing scoring systems. Last, we used the best cutoff value (88.16) for severe acute pancreatitis (SAP) to determine the risk stratification classification.

RESULTS

Age, the reduction in apolipoprotein A1 and the presence of pleural effusion were independent risk factors for SAP and were used to construct the nomogram (risk prediction score referred to as AAP). The concordance index of the nomogram in the development and validation groups was 0.930 and 0.928, respectively. Calibration plots demonstrate excellent agreement between the predicted and actual probabilities in SAP patients. The area under the curve of the nomogram (0.929) was better than those of the Bedside Index of Severity in AP (BISAP), Ranson, Acute Physiology and Chronic Health Evaluation (APACHE II), modified computed tomography severity index (MCTSI), and early achievable severity index scores (0.852, 0.825, 0.807, 0.831 and 0.807, respectively). In comparison with these scores, the integrated discrimination improvement and decision curve analysis showed improved accuracy in predicting SAP and better net benefits for clinical decisions. Receiver operating characteristic curve analysis was used to determine risk stratification classification for AHTGP by dividing patients into high-risk and low-risk groups according to the best cutoff value (88.16). The high-risk group (> 88.16) was closely related to the appearance of local and systemic complications, Ranson score ≥ 3, BISAP score ≥ 3, MCTSI score ≥ 4, APACHE II score ≥ 8, C-reactive protein level ≥ 190, and length of hospital stay.

CONCLUSION

The nomogram could help identify AHTGP patients who are likely to develop SAP at an early stage, which is of great value in guiding clinical decisions.

CT Characteristics of Acute Pancreatitis with Preexisting Fatty Liver and Its Impact on Pancreatitis Severity and Persistent Systemic Inflammatory Response Syndrome

Purpose

To study the CT characteristics of acute pancreatitis (AP) associated with preexisting fatty liver (FL) and the impact of preexisting FL on the severity of AP and persistent systemic inflammatory response syndrome (SIRS).

Patients and Methods

A total of 189 patients with AP were divided into AP with and without preexisting FL. The CT features, clinical characteristics, severity of AP, and presence of persistent SIRS between the two groups were compared. Univariate and multivariate analyses were performed to determine the risk factors for predicting SIRS. The diagnostic performances of the risk factors were evaluated by receiver operating characteristic (ROC) curve analysis.

Results

Among the 189 patients, 49.7% (94/189) had preexisting FL. On CT, AP patients with preexisting FL were more likely to develop necrosis (23.4% vs 10.5%, p=0.021), local complications (45.7% vs 29.5%, p=0.025) and persistent SIRS (59.6% vs 27.4%, p<0.001). Multivariate analysis showed that preexisting FL (OR=2.863, 95% CI: 1.264–6.486, p=0.012), APACHE II≥6 (OR=1.334, 95% CI: 1.117–1.594, p=0.002), and MCTSI ≥4 (OR=1.489, 95% CI: 1.046–2.119, p=0.027) could be independent risk factors for persistent SIRS. The areas under the ROC curve of preexisting FL, APACHE II, and MCISI in diagnosing AP patients with persistent SIRS were 0.664, 0.703, and 0.783, respectively.

Conclusion

Patients with preexisting FL were more likely to develop necrosis and local complications on CT and present more severe AP and persistent SIRS. Preexisting FL can be an independent risk factor in predicting the presence of persistent SIRS in patients with AP.

Heart Disease and Stroke Statistics-2022 Update: A Report From the American Heart Association

BACKGROUND

The American Heart Association, in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, diet, and weight) and health factors (cholesterol, blood pressure, and glucose control) that contribute to cardiovascular health. The Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, congenital heart disease, rhythm disorders, subclinical atherosclerosis, coronary heart disease, heart failure, valvular disease, venous disease, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs).

METHODS

The American Heart Association, through its Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States to provide the most current information available in the annual Statistical Update. The 2022 Statistical Update is the product of a full year's worth of effort by dedicated volunteer clinicians and scientists, committed government professionals, and American Heart Association staff members. This year's edition includes data on the monitoring and benefits of cardiovascular health in the population and an enhanced focus on social determinants of health, adverse pregnancy outcomes, vascular contributions to brain health, and the global burden of cardiovascular disease and healthy life expectancy.

RESULTS

Each of the chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics.

CONCLUSIONS

The Statistical Update represents a critical resource for the lay public, policymakers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.

Early prediction of severe acute pancreatitis using machine learning

BACKGROUND

Acute pancreatitis (AP) is one of the most common causes of gastrointestinal-related hospitalizations in the United States. Severe AP (SAP) is associated with a mortality rate of nearly 30% and is distinguished from milder forms of AP. Risk stratification to identify SAP cases needing inpatient treatment is an important aspect of AP diagnosis.

METHODS

We developed machine learning algorithms to predict which patients presenting with AP would require treatment for SAP. Three models were developed using logistic regression, neural networks, and XGBoost. Models were assessed in terms of area under the receiver operating characteristic (AUROC) and compared to the Harmless Acute Pancreatitis Score (HAPS) and Bedside Index for Severity in Acute Pancreatitis (BISAP) scores for AP risk stratification.

RESULTS

61,894 patients were used to train and test the machine learning models. With an AUROC value of 0.921, the model developed using XGBoost outperformed the logistic regression and neural network-based models. The XGBoost model also achieved a higher AUROC than both HAPS and BISAP for identifying patients who would be diagnosed with SAP.

CONCLUSIONS

Machine learning may be able to improve the accuracy of AP risk stratification methods and allow for more timely treatment and initiation of interventions.

Secondary Causes of Hypertriglyceridemia are Prevalent Among Patients Presenting With Hypertriglyceridemia Induced Acute Pancreatitis

BACKGROUND

Hypertriglyceridemia induced acute pancreatitis (HIAP) is the third common cause of acute pancreatitis. HIAP can result in recurrent attacks of severe AP with significant morbidity and mortality. Hypertriglyceridemia (HTG) could be primary or secondary. Although genetic causes of HTG are well studied, the prevalence of secondary causes of HTG in patients presenting with HIAP is not well characterized. This study aimed to identify the prevalence of risk factors for secondary hypertriglyceridemia among patients presenting with HIAP in a tertiary referral center in a large metropolitan area.

METHODS

This is a retrospective analysis of all patients admitted with AP from August 2012-2017. A subgroup of patients with triglycerides >880 mg/dl were included for analysis. Secondary causes of HTG were identified. Secondary analysis evaluating the severity of pancreatitis was performed.

RESULTS

There were 3,746 patients admitted for AP of which 57 patients had AP and HTG. Of these 57 patients, 70.2% had history of diabetes mellitus, 26.3% had history of heavy alcohol use, 22.8% had chronic kidney disease, 47.3% with obesity, and 21.1% with metabolic syndrome. Two patients were classified as unexplained HTG. Secondary analysis showed a total of 45.6% of patients requiring ICU admission. 26.3% of patients with severe inflammatory pancreatitis and 17.5% of patients with severe necrotizing pancreatitis.

CONCLUSIONS

In our cohort of HIAP, 55 out of 57 patients had secondary causes for HTG. Identifying secondary causes of HTG during acute hospitalization is important to tailor outpatient treatment in order to prevent future admissions with HIAP.

Dynamic changes of lipid profile in severe hypertriglyceridemia-induced acute pancreatitis patients under double filtration plasmapheresis: a retrospective observational study

Background

To investigate the dynamic change of lipid profile under double filtration plasmapheresis (DFPP) in severe hypertriglyceridemia-induced acute pancreatitis (sHTGP) patients and ascertain the association between these changes and the clinical prognosis.

Methods

sHTGP patients admitted within 72 h after disease onset were included, and all the patients received DFPP within 24 h after admission. Lipid profile were detected on admission, consecutive 4 days after DFPP and at discharge.

Results

There were 47 sHTGP patients enrolled in this study. At admission, all the parameters of lipid profile changed significantly except for low density lipoprotein. In the first day after DFPP, the serum level of TG, cholesterol and very low density lipoprotein declined significantly, while the high-density lipoprotein (HDL) as well as apoprotein A1 elevated obviously (P < 0.05). TG maintained the downward trend in the following three days and the other parameters kept steady. Linear regression analysis showed that HDL was negatively correlated with the duration of hospitalization among three adjusted models (P = 0.043, P = 0.029, P = 0.025 respectively).

Conclusion

There was distinct fluctuation of the lipid profile upon the burst of sHTGP and the parameters changed significantly in the first day after DFPP. Among these parameters, HDL may serve as a biomarker for disease prognosis in patients with sHTGP.