Potential causal relationship between body mass index (BMI) and acute pancreatitis: a Mendelian randomization study

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Method: Analyze the aggregated data from the gene wide association study (GWAS). The GWAS summary data used were all sourced from the European population. Using single nucleotide polymorphisms significantly correlated with body mass index levels as instrumental variables, a two sample Mendelian randomization analysis was performed using inverse variance weighted (IVW), MR Egger regression, and weighted median (WM) methods, respectively, to evaluate the causal effect of body mass index and acute pancreatitis using odds ratio (Oddsratio, OR). The body mass index data as an exposure factor was obtained from individuals of European ancestry in the UK Biobank (n=454884). We also used GWAS's publicly aggregated statistical dataset for self-reported non cancer disease codes: acute pancreatitis data included in the UK Biobank (n=463010)（ http://www.nealelab.is/uk-biobank ））as the ending. Result: The IVW results showed that BMI was associated with acute pancreatitis（ β= 0.001969, se=0.0004278, P=0.000004189) showed a significant correlation, and the weighted median results were consistent with the IVW result（ β= 0.001901, se=0.0008264, P=0.02142), also confirming the causal relationship between BMI and acute pancreatitis. The MR Egger regression results confirm that directional pleiotropy is unlikely to bias the results (intercept=4.6E-07; P=0.983), but there is no causal relationship between BMI and acute pancreatitis（ β= 0.001943, se=0.001326, P=0.1442). Cochran's Q-test and funnel plot indicate no evidence of heterogeneity and asymmetry, indicating the absence of directed pleiotropy. Conclusion: The results of MR analysis support a causal relationship between body mass index (BMI) and an increased risk of acute pancreatitis. body mass index acute pancreatitis Mendelian randomization correlation risk of onset Figures Figure 1 Figure 2 Figure 3 Figure 4 Full Text Acute pancreatitis (AP) is a common inflammatory disease caused by multiple pathogenic factors, with clinical manifestations mostly including nausea, fever, and severe abdominal pain [ 1 – 3 ] . The etiology of AP is complex, the condition develops rapidly, and treatment is difficult. Patients with more severe conditions can quickly develop into severe acute pancreatitis, with a higher mortality rate. The most common causes are biliary diseases, hyperlipidemia, and alcohol consumption. Among them, the occurrence of hyperlipidemic pancreatitis (HLP) is not related to serum cholesterol levels, but closely related to a significant increase in serum triglyceride (TG) levels, hence it is also known as hypertriglyceridemic pancreatitis (HTGP) [ 4 ] . In recent years, the incidence rate of HTGP is on the rise, and often leads to more serious clinical processes. A large sample study in Jiangxi Province, China analyzed the incidence trend of AP from 2005 to 2012, showing that hyperglycemia and blood lipids have surpassed alcohol as the second leading cause of biliary diseases (14.3%), and the mortality rate of severe HTPG is significantly higher than that of severe biliary pancreatitis [ 5 ] . A large-scale study in developed regions of China shows that the proportion of hyperlipidemia in the etiology of AP has jumped to 25.6% [ 6 ] . In addition, HTGP mostly occurs in young men, especially in patients with obesity, alcoholism and diabetes [ 7 – 9 ] . Although people's cognition of AP has been greatly improved in recent years, its incidence rate is still rising year by year, especially the case fatality rate of severe acute pancreatitis has reached as high as 20% -50% [ 10 ] . Active research on the correct methods of prevention and early assessment of AP can reduce the incidence rate of AP and the mortality rate after turning to severe acute pancreatitis [ 11 ] . In recent years, the incidence of obesity and hyperlipidemia in the Chinese population has been increasing year by year. Numerous studies have found that obesity is one of the risk factors for AP. There are also studies indicating that obesity can be one of the indicators for evaluating the severity of AP [ 12 ] . As BMI serves as a standard for assessing obesity, it also has the potential to serve as an indicator for predicting the risk of developing AP. Mendelian randomization (MR) is a new method of inferring causal effects that has emerged in recent years [ 13 ] . This method uses genetic variation as the instrumental variable and explores the causal relationship between exposure and outcomes based on the strong correlation between instrumental variables and exposure factors [ 14 – 16 ] . This study will explore the causal relationship between BMI and AP through two sample Mendelian randomization methods. Materials and Methods This study adopts a two sample Mendelian randomization method, using single nucleotide polymorphism (SNP) loci related to BMI levels as instrumental variables to explore the causal relationship between BMI and the onset of AP. In this MR study, the selected working variables need to meet the following three assumptions: ① Genetic variation is related to BMI level; ② Genetic variation must not be associated with any confounding factors affecting AP, nor with confounding factors on the exposure outcome pathway; ③ Genetic variation only affects outcomes through BMI (Figure 1). 1.1 Selection of data sources and instrumental variables MR Base Database（ http://www.mrbase.org/ ）is a large collection of statistical data containing hundreds of genome-wide association studies (GWAS). We searched using the publicly available GWAS meta-analysis summary statistical dataset and analyzed BMI data of European descent (n=454884) as exposure factors. We screened GWAS data and included SNPs significantly correlated with BMI. Based on the results of BMI meta-analysis, SNPs with a correlation of P<5E-08 were included as instrumental variables in this study; And in order to avoid the impact of linkage imbalance (LD) in SNP on the analysis results, the parameter r 2 threshold is set to 0.001 and the distance is set to 10000 kb. In order to ensure a strong correlation between instrumental variables and endogenous variables and avoid weak instrumental bias, we calculated r 2 for each SNP, representing the proportion of variance explained by the instrumental variable SNP; The F-statistic is used to evaluate the strength of the instrumental variable based on the P-value threshold of 5.00E-08 (whole genome significance). Genetic variations related to BMI were screened as instrumental variables (IV), and then two sample MR analysis was performed. Finally, 241 SNPs were included as IVs. We also used publicly published acute pancreatitis data from the UK Biobank (n=463010)（ http://www.finngen.fi/en ）As a result. To avoid bias due to population stratification, all the genetic data we selected were sourced from the European population. 1.2 Statistical analysis Mendelian randomization is the use of genetic variation in non experimental data to estimate the causal relationship between exposure and outcome. This MR study mainly uses the inverse variance weighted method (IVW) to explore the causal relationship between BMI level and AP. We used both the weighted median method (WM) and the Mendelian randomization method (MR Egger) based on Egger regression to perform sensitivity analysis on the statistical results. IVW is considered the standard method for summarizing data in MR. This method uses the Wald ratio method to estimate the causal effects of each included tool SNP, and then performs a weighted summary analysis. The weighted median estimation method only requires that at least 50% of the weights contributed by genetic variation are valid for statistical calculation. MR Egger regression can detect and correct for pleiotropy issues, requiring the included instrumental variables to satisfy the InSIDe hypothesis (Instrument strength independent of direct effect, InSIDE), which assumes that the correlation between tool exposure and tool outcome is independent. A P-value less than 0.05 is considered statistically significant for testing. All MR analyses were conducted on the MR Base platform (application version 1.2.1 e646be [June 27, 2018], R version 3.5.0). 1.3 Heterogeneity and Sensitivity Testing We evaluated the heterogeneity between SNPs using Cochran's Q-statistic and I 2 statistic, and if P>0.05, it indicates no significant heterogeneity. At the same time, we also excluded and included SNPs one by one by leaving one method to observe whether it had an impact on the analysis results, and drew a forest map. If a certain SNP was excluded and P>0.05 was obtained, it is considered that SNPs will not have a significant impact on the results. In terms of pleiotropy, we used the intercept term of MR Egger regression and Mendelian random pleiotropy residual test to include the level pleiotropy of SNP. In MR Egger regression, if the intercept tends to 0, it can be considered that there is no pleiotropy in the results. Results 2.1 Mendelian Randomization Analysis Tool Variable Screening Results We screened 241 independent SNPs from BMI's GWAS data as IVs ( Table S1、S2). These are significantly correlated with BMI throughout the entire genome (Table 1, Figure 1). The results showed that 241 SNPs were positively correlated with AP, and the results were statistically significant (Table 1, Table S1). The exposure variance (r 2 statistic) of 2.2E-05 is caused by genetic variation in IVs. A P-value of 5.00E-08 corresponds to a F-statistic value of>30 for each variable, and when the F-value is less than 10, it is defined as weak IVs. Therefore, weak instrument bias can be ignored. 2.2 Mendelian Randomization Analysis Results The IVW method results confirm a causal relationship between BMI levels and AP（ β= 0.001969, SE=0.0004278, P=0.000004189; Table 1, Figures 1 and 2). The results of the weighted median method also indicate a causal relationship between BMI and AP（ β= 0.001901, SE=0.0008264, P=0.02142; Table 1, Figures 1 and 2). The intercept represents the average pleiotropic effect of genetic variation (the direct impact of variation on the average outcome). If the intercept approaches 0, it can be considered that there is no horizontal pleiotropy. The MR Egger regression results showed that directional pleiotropy had almost no impact on the results (intercept=4.6E-07; P=0.983). MR Egger analysis results showed no causal correlation between BMI and AP（ β= 0.001943, SE=0.001326, P=0.1442; Table 1, Figures 1 and 2). In summary, the results of MR analysis are likely to support the potential causal relationship between BMI and AP. And due to three methods β The values are all greater than 0, indicating that BMI is likely to be an unfavorable factor affecting the onset of AP. 2.3 Heterogeneity and Sensitivity Analysis In terms of sensitivity analysis, Cochran's Q test showed no significant heterogeneity among the instrumental variables (P>0.05). The retention method showed no significant difference in statistical results after excluding the included SNPs (P>0.05). The symmetry of the funnel plot and the intercept of each group in the MR Egger regression test results are equal to or close to 0, and P>0.05 indicates that the results do not have directional level pleiotropy, which also indicates that the MR analysis results are almost unbiased. The specific results are shown in Table 1, Figure 3, and Figure 4. Discussion Exploring the causal relationship between exposure factors and AP is crucial for disease prevention, early diagnosis, and optimization of treatment plans. Although randomized controlled trials (RCTs) are the "gold standard" for clarifying causal relationships, completing RCTs requires a significant amount of manpower and material resources, and in most cases, it is also ethical [17] . Therefore, we can use Mendelian randomization methods. Mendelian randomization revolves around Mendelian's second law, where parents with two or more pairs of relative traits hybridize to produce gametes in their offspring [18] . At the same time as allele segregation, genes on non homologous chromosomes exhibit free combination. The statistical essence is to determine causal relationships by screening and utilizing instrumental variables [19,20] . In this study, we analyzed whether there is a causal correlation between BMI and AP through Mendelian randomization research, and found that there is a causal correlation between BMI level and the risk of AP occurrence, which is consistent in the IVW and WM methods. And a high BMI level is a risk factor that promotes the occurrence of AP. The data included in this study were all from large-scale GWAS studies, and no significant heterogeneity or pleiotropy was found between instrumental variables, which ensured the stability and credibility of the results. Acute pancreatitis is caused by the activation of a large amount of trypsin in patients, which promotes the digestion of pancreatic tissue and ultimately leads to necrosis of the pancreatic region and adjacent tissues [21] . For patients with organ failure and other concurrent symptoms, it is called severe acute pancreatitis. Due to the rapid development of the condition and high mortality rate of these patients. Therefore, how to evaluate the severity of its condition has become one of the hot topics in clinical research. It is currently believed that the presence of hyperglycemia at admission is associated with poor prognosis in patients with acute pancreatitis, and obesity is also considered a risk factor for severe acute pancreatitis (SAP) [22-25] . Therefore, measuring BMI can become an important indicator for predicting the risk of AP occurrence and evaluating patient prognosis. Two prospective cohort studies from Copenhagen included 118085 participants and measured their baseline BMI values. The results showed that the higher the BMI, the higher the risk of acute pancreatitis [26] . Compared to individuals with a BMI of 18.5-24.9, the risk ratio for subjects with a BMI of 25-29.9 was 2.1 (1.6-2.9), and the risk ratio for subjects with a BMI>35 was 2.8 (1.8-4.3). In the age and gender adjusted models, the correlation between triglyceride mediated BMI and the risk of acute pancreatitis was 29% (95% CI: 12-46%, P=0.001), while in the multivariate adjusted model it was 22%. This indicates that higher BMI is associated with a higher risk of acute pancreatitis in the general population, partially mediated by higher triglycerides. According to literature reports, since 2000, the number of inpatients in the United States due to pathological obesity has increased by about 314%, and the incidence rate of acute pancreatitis (AP) closely related to obesity has also shown an increasing trend [27] . In 2009, there were more than 274000 inpatients in the United States due to AP, more than twice as many as in 1988. The rise in the incidence rate of AP almost coincides with the global obesity epidemic, suggesting that obesity may be related to the incidence of AP. Another prospective cohort study involving 117531 Danes also explored the correlation between BMI and the onset of acute pancreatitis [28] . Based on BMI levels, participants were divided into five groups: Group 1 (underweight group): BMI<18.5kg/m 2 , Group 2 (healthy weight group, also control group): 18.5kg/m 2 ≤ BMI ≤ 24.9kg/m 2 , Group 3 (super recombination): 25kg/m 2 ≤ BMI ≤ 29.9kg/m 2 , Group 4 (Grade 1 obesity group): 30kg/m 2 ≤ BMI ≤ 34.9kg/m 2 , Group 5 (Grade 2 and 3 obesity groups): BMI ≥ 35kg/m 2 . The blood lipid measurement results of participants, as well as other characteristic data such as smoking, drinking, exercise, and medication use, were collected by the researchers. Participants with a history of acute pancreatitis were not included in this study. The research results showed that during a median follow-up period of 8 years, 458 out of 117531 participants developed acute pancreatitis. And researchers found that a BMI of 22kg/m 2 is a node, and when BMI ≥ 22kg/m 2 , the risk of acute pancreatitis in participants increases correspondingly with the increase of BMI level. And after adjusting for multiple variables, the risk of acute pancreatitis was 1.4 times (HR 1.4, 95% CI 1.1-1.8), 2.1 times (HR 2.1, 95% CI 1.6-2.9), and 2.8 times (HR 2.8, 95% CI 1.8-4.3) higher in the overweight group (25-29.9kg/m 2 ), grade 1 obesity group (30-34.9kg/m 2 ), and grade 2 and 3 obesity group (>35kg/m 2 ) compared to the healthy weight group, respectively, with a trend P-value of 2X10 -8 . The research team speculates that high levels of triglycerides are significantly associated with an increased risk of acute pancreatitis. For every 1mmol/L increase in triglycerides, the risk of acute pancreatitis increases by about 12%, and the effect of triglycerides on the increased risk of acute pancreatitis is more significant in women compared to men (P=0.021). In addition, the study also found that smoking is an independent risk factor for acute pancreatitis, with smoking patients having a 36% increased disease risk compared to non-smokers. The mechanism is not yet fully understood, possibly due to the toxicity of free fatty acids produced during the metabolic decomposition of triglycerides. In vitro experiments have confirmed that unsaturated fatty acids have pro-inflammatory effects, release intracellular calcium, interfere with mitochondrial function, and lead to cell necrosis, but saturated fatty acids do not [29] . In the case of hypertriglyceridemia, the increase of macromolecular substances such as chylomicrons and extremely low density lipoprotein may block pancreatic capillaries, alter the structure of acinar cells, and ultimately trigger the release of pancreatic enzyme and lipase, leading to related symptoms of acute pancreatitis. And in response to the issue of high triglyceride induced acute pancreatitis, there are currently studies that have confirmed that insulin significantly reduces serum TG levels by promoting the expression of LPL gene mRNA, activating lipoprotein esterase, and accelerating the degradation of chylomicrons in patients with high triglyceride induced acute pancreatitis [30] ; The overall prognosis can be improved by improving glucose metabolism disorders and reducing the free radicals produced by glucose metabolism disorders in such patients. For some patients, when diagnosed with acute pancreatitis, the levels of triglycerides in their bodies are tested to be higher than 1000mg/dL, and the absolute majority of them have a good prognosis after intensive insulin treatment, especially when the triglyceride levels in these patients decrease to below 500mg/dL after 3-5 insulin treatment, the clinical symptoms of the patients will significantly improve [31-33] . Therefore, when the level of triglycerides is below 500mg/dL, insulin treatment can be stopped. In order to prevent the recurrence and progression of acute pancreatitis, long-term testing of triglyceride levels should be carried out to maintain them below 200mg/dL [34] . In summary, prospective studies in both Europe and the Americas have shown a correlation between higher BMI and the risk of developing AP, but a study from Sweden concluded that BMI is not associated with the risk of developing AP [35] . The research results of this article are consistent with most prospective research conclusions. This article also has certain limitations. For example, we only found that a high BMI value increases the risk of AP occurrence, but failed to determine the specific trend of AP occurrence risk within different BMI levels. Therefore, studying the impact of different BMI value ranges on the risk of AP occurrence may have more clinical guiding significance. In the future, large-scale, standardized clinical trials and related MR analysis are still needed to conduct more in-depth research on the impact and clinical significance of BMI levels within different ranges on the risk of AP occurrence and prognosis. Declarations Conflicts of Interest: The authors declare no conflict of interest. Funding: This study did not receive external funding. Author Contribution Siqi Yang is responsible for analyzing article data, using software, and writing articles;Qiao Chi is responsible for reviewing articles and analyzing data results;Waxing Wang is responsible for controlling the quality of articles and proofreading them. Data Availability Statement: The data that support the findings of this study are openly available in Finnish database at http://www.finngen.fi/en and GWAS database at https://gwas.mrcieu.ac.uk . References Peter AB, Thomas LB, Christos D, Hein GG, Colin DJ, Michael GS, et al. Classification of acute pancreatitis—2012: revision of the Atlanta classification and definitions by international consensus. Gut (2013) 62:102–111. doi: 10.1136/gutjnl-2012-302779 Rebecca AN, Daniel KB, Ruth EP, Gary LB. Increased Risk of Acute Pancreatitis and Biliary Disease Observed in Patients With Type 2 Diabetes: A retrospective cohort study. Diabetes Care (2009) 32:834. doi: 10.2337/dc08-1755 Maxim SP, Satyanarayan S, Mandira C, Anthony RJP, John AW. Organ failure and infection of pancreatic necrosis as determinants of mortality in patients with acute pancreatitis. Gastroenterology (2010) 139:813–820. doi: 10.1053/j.gastro.2010.06.010 Wayne T, Udayakumar N, Luis R, Joseph BP, Andres G. Hypertriglyceridemic pancreatitis: presentation and management. Am J Gastroenterol (2009) 104:984–991. doi: 10.1038/ajg.2009.27 He WH, Lu NH. Emergent Triglyceride-Lowering Therapy for Hypertriglyceridemic Pancreatitis. Hepatogastroenterology (2015) 62:429–434. doi: 10.1097/MCG.0000000000000552 Lu ZH, Chen YL, Wu YS, Lin YJ, Yang N, Wang XL, et al. The role of double filtration plasmapheresis in hypertriglyceridemic pancreatitis: A propensity score matching analysis. J Clin Apher (2020) 35:388–397. doi: 10.1002/jca.21811 Yu BJ, He WH, He C, Li NS, Li JR, Zhu Y, et al. Low-Molecular-Weight Heparin Combined With Insulin Versus Insulin Alone in the Treatment of Hypertriglyceridemic Pancreatitis (LIHTGP Trial): Study Protocol for a Multicenter, Prospective, Single-Blind, Randomized Controlled Trial. Pancreas (2020) 49:1383–1387. doi: 10.1097/MPA.0000000000001784 Jakob J, Jadranka BP, Karmen R, Rafael P. Factors Affecting Outcome in Acute Hypertriglyceridemic Pancreatitis Treated with Plasma Exchange: An Observational Cohort Study. Plos One (2014) 9:e102748. doi: 10.1371/journal.pone.0102748 Chen WJ, Sun XF, Zhang RX, Xu MJ, Dou TH, Zhang XB, et al. Hypertriglyceridemic acute pancreatitis in emergency department: Typical clinical features and genetic variants. J Dig Dis (2017) 18:359–368. doi: 10.1111/1751-2980.12490 Lu ZH, Chen YL, Wu YS, Lin YJ, Yang N, Wang XL, et al. The role of double filtration plasmapheresis in hypertriglyceridemic pancreatitis: A propensity score matching analysis. J Clin Apher (2020) 35:388–397. doi: 10.1002/jca.21811 Michael AM, Howard AR, Mark DG. Acute Pancreatitis: A Review. JAMA (2021) 325:382–390. doi: 10.1001/jama.2020.20317 Greenberg JA, Hsu J, Bawazeer M, Marshall J, Friedrich JO, Nathens A, et al. Clinical practice guideline: management of acute pancreatitis. Can J Surg (2016) 59:128–140. doi: 10.1503/cjs.015015 Shah E, George DS. Mendelian randomization: can genetic epidemiology help redress the failures of observational epidemiology? Hum Genet (2008) 123:15–33. doi: 10.1007/s00439-007-0448-6 Duncan CT, David VC. Commentary: The concept of 'Mendelian Randomization'. Int J Epidemiol (2004) 33:21–25. doi: 10.1093/ije/dyh048 Cosetta M, John RT, Martin DT, Keith RA. An integrated approach to the meta-analysis of genetic association studies using Mendelian randomization. Am J Epidemiol (2004) 160:445–452. doi: 10.1093/aje/kwh228 Smith GD, Ebrahim S. Mendelian randomization: prospects, potentials, and limitations. Int J Epidemiol (2004) 33:30–42. doi: 10.1093/ije/dyh132 Farkas G. Inflammatory mediators in acute pancreatitis (theoretical considerations). Orv Hetil (1995) 136:1819–1822. doi: 10.3748/wjg.v10.i19.2898 Caroline LR, George DS. Two-step epigenetic Mendelian randomization: a strategy for establishing the causal role of epigenetic processes in pathways to disease. Int J Epidemiol (2012) 41:161–76. doi: 10.1093/ije/dyr233 Murielle B, Valentin R. Usefulness of Mendelian Randomization in Observational Epidemiology. Int J Environ Res Public Health (2010) 7:711–728. doi: 10.3390/ijerph7030711 He WJ, John C, Elizabeth AW, Jason FO, Sunil KA. Mendelian randomization: potential use of genetics to enable causal inferences regarding HIV-associated biomarkers and outcomes. Curr Opin HIV AIDS (2010) 5:549–559. doi: 10.1097/COH.0b013e32833f2087 Jolanta J, Joanna B, Piotr P, Romana T, Jerzy S, Ryszard S, et al. Leptin protects the pancreas from damage induced by caerulein overstimulation by modulating cytokine production. Pancreatol (2002) 2:89–99. doi: 10.1159/000055897 Jiang K, Huang W, Yang XN, Xia Q. Present and future of prophylactic antibiotics for severe acute pancreatitis. World J Gastroenterol (2012) 18:279–284. doi: 10.3748/wjg.v18.i3.279 Marta P, Grazyna R, Janusz M, Stanisław O, Mariusz F, Jerzy W, et al. The results of severe acute pancreatitis treatment with continuous regional arterial infusion of protease inhibitor and antibiotic: a randomized controlled study. Pancreas (2010) 39:863–867. doi: 10.1097/MPA.0b013e3181d37239 Wu XM, Ji KQ, Wang HY, Li GF, Zang B, Chen WM. Total enteral nutrition in prevention of pancreatic necrotic infection in severe acute pancreatitis. Pancreas (2010) 39:248. doi: 10.1097/MPA.0b013e3181bd6370 Vege SS, Baron TH. Management of pancreatic necrosis in severe acute pancreatitis. Clin Gastroenterol Hepatol (2005) 3:192–196. DOI: 10.1016/s1542-3565(04)00668-8 Ahmet T, Abdullah B, Mehmet HT, Ercan G, Abdurrahim D, İbrahim K, et al. The Predict and Prognosis of Ghrelin and BMI in Acute Pancreatitis. Int J Surg (2014) 12:1310–1313. doi: 10.1016/j.ijsu.2014.10.040 Georgios IP, Dionysios JP, Haritha A, Adam S, David CW. Obesity increases the severity of acute pancreatitis: performance of APACHE-O score and correlation with the inflammatory response. Pancreatol (2006) 6:279–285. doi: 10.1159/000092689 Biswajit K, Bara EK, Vijay PS. Obesity and acute pancreatitis. Curr Opin Gastroenterol (2017) 33:374–382. doi: 10.1097/MOG.0000000000000386 Stephen JP, Roberta AG. Calcium, mitochondria and the initiation of acute pancreatitis. Pancreatology (2022) 22:838–845. doi: 10.1016/j.pan.2022.07.011 Han P, Wei GH, Cai K, Xiang X, Deng WP, Li YB, et al. Identification and functional characterization of mutations in LPL gene causing severe hypertriglyceridaemia and acute pancreatitis. J Cell Mol Med (2020) 24:1286–1299. doi: 10.1111/jcmm.14768 Refaat AH, Tiffany D. Prebiotics in Immuno-Modulation for Treatment of Acute Pancreatitis. World J Gastroenterol (2014) 20:16101–16105. doi: 10.3748/wjg.v20.i43.16101 Shen JQ, Shen J, Wang XP. Expression of insulin-like growth factor binding protein-4 (IGFBP-4) in acute pancreatitis induced by l-arginine in mice. Acta Histochem (2012) 114:379–385. doi: 10.1016/j.acthis.2011.07.008 Madan RA, Naba RM, Shobhit G, Manoj S. Acute pancreatitis owing to very high triglyceride levels treated with insulin and heparin infusion. Bmj Case Rep (2013) 22:bcr2013008550. doi: 10.1136/bcr-2013-008550 Yu B, He W, He C. Low-Molecular–Weight Heparin Combined With Insulin Versus Insulin Alone in the Treatment of Hypertriglyceridemic Pancreatitis (LIHTGP Trial): Study Protocol for a Multicenter, Prospective, Single-Blind, Randomized Controlled Trial. Pancreas (2021) 50:e40. doi: 10.1097/MPA.0000000000001784 Kerstin BB, Anders S, Gunnar S, Bengt EW. Glibenclamide and obesity may be risk-factors for acute pancreatitis. Diabetes Care (2002) 25:298–302. doi: 10.2337/diacare.25.2.298 Tables Table 1 Evaluation of the causal relationship between BMI and the risk of acute pancreatitis using each MR analysis method MR method Number of SNPs Beta SE 95% confidence interval Association P-value Cochran Q statistic I2 Heterogeneity P-value Inverse-variance weighted 241 0.001969 0.0004278 0.00187055–0.00206745 0.000004189 220.9 0.007 0.8068 MR Egger 241 0.001943 0.001326 0.00184585–0.00204015 0.1442 220.9 0.004 0.7939 Weighted median 241 0.001901 0.0008264 0.00180595–0.00199605 0.02142 / / / Additional Declarations No competing interests reported. Supplementary Files S3.csv Table S1 S4.csv Table S2 Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3925941","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":271058852,"identity":"4caf29f2-ad03-46e4-b995-3f179fae1e57","order_by":0,"name":"Si-Qi Yang","email":"","orcid":"","institution":"Renmin Hospital of Wuhan University","correspondingAuthor":false,"prefix":"","firstName":"Si-Qi","middleName":"","lastName":"Yang","suffix":""},{"id":271058853,"identity":"1b6e460d-72ec-401d-91bf-108c0b0d4fa4","order_by":1,"name":"Qiao Shi","email":"","orcid":"","institution":"Renmin Hospital of Wuhan University","correspondingAuthor":false,"prefix":"","firstName":"Qiao","middleName":"","lastName":"Shi","suffix":""},{"id":271058854,"identity":"1af1ce58-394f-4693-99a3-cc0d17acdd03","order_by":2,"name":"Wei-Xing Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyUlEQVRIiWNgGAWjYNCCAgseBgbmAwc+/CBKOTMQG0jw8DCwJR6c2UOCFgYeBh7jwxxsRGiQbz9/8AFQi4w9e8+Hw0CN8vxiB/BrMTiTzGwAdhjP2Q2HCywYDGfOTiCghSGZTQKsRSJ3w+EZPAwJBrcJaJHvf8z+A6xF/s2DwzxsRGhhuJHMBgkxCR4G4rQY3HhsLJEA0nImzQAYyBKE/SLfn/jww4cKG3v29sOPP3z4YSPPL03IYSCApEaCCOWjYBSMglEwCggCALS7OpBrFC80AAAAAElFTkSuQmCC","orcid":"","institution":"Renmin Hospital of Wuhan University","correspondingAuthor":true,"prefix":"","firstName":"Wei-Xing","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2024-02-04 02:44:03","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3925941/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3925941/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":50817279,"identity":"6995bd88-5b65-4459-8835-c08fbf514010","added_by":"auto","created_at":"2024-02-07 20:03:43","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":394456,"visible":true,"origin":"","legend":"\u003cp\u003eForest map of the causal effects of single nucleotide polymorphisms associated with BMI on acute pancreatitis.\u003c/p\u003e\n\u003cp\u003eThe meaning of the red line is the MR results of the MR Egger test and IVW method\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3925941/v1/56608c09b8611c247e48a8a3.jpg"},{"id":50817278,"identity":"0e915cc6-b5f1-4592-ba86-463540e87f34","added_by":"auto","created_at":"2024-02-07 20:03:43","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":165851,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plot of genetic association between BMI and acute pancreatitis. The slope of each line represents the causal relationship of each method.\u003c/p\u003e\n\u003cp\u003eThe blue line represents the inverse variance weighted estimation, the green line represents the weighted median estimation, and the deep blue line represents the Mendelian randomization Egger estimation.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3925941/v1/6d85b7219e7934c73273dacc.jpg"},{"id":50817281,"identity":"e3e15976-0436-4c8c-8b1d-f53596a8d608","added_by":"auto","created_at":"2024-02-07 20:03:43","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":114078,"visible":true,"origin":"","legend":"\u003cp\u003eEvaluate heterogeneity using funnel plots.\u003c/p\u003e\n\u003cp\u003eThe blue line represents the inverse variance weighted estimation, while the deep blue line represents the Mendelian randomization Egger estimation\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3925941/v1/548322f436e4f3d6821f8771.jpg"},{"id":50817284,"identity":"2dac451a-511c-4164-b335-4becb048afbe","added_by":"auto","created_at":"2024-02-07 20:03:43","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":506306,"visible":true,"origin":"","legend":"\u003cp\u003eHeterogeneity of analysis results using \"leave on one out\" results\u003c/p\u003e\n\u003cp\u003eThe red line represents the final summary result\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3925941/v1/9efeca3f0ef319f47d71113b.jpg"},{"id":51206800,"identity":"7fffb663-5f55-4ba0-af36-44fe8e356c6d","added_by":"auto","created_at":"2024-02-16 02:58:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":636902,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3925941/v1/3fe3d486-b980-490e-9a1b-5bb9e047652c.pdf"},{"id":50817726,"identity":"8e7310f5-1ac5-41f7-8c45-1fa711ea5194","added_by":"auto","created_at":"2024-02-07 20:11:43","extension":"csv","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":53023,"visible":true,"origin":"","legend":"\u003cp\u003eTable S1\u003c/p\u003e","description":"","filename":"S3.csv","url":"https://assets-eu.researchsquare.com/files/rs-3925941/v1/b51b0069129dd24a304a7fd9.csv"},{"id":50817282,"identity":"fb24d924-da46-4fed-95df-068c63528b1f","added_by":"auto","created_at":"2024-02-07 20:03:43","extension":"csv","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":122979,"visible":true,"origin":"","legend":"\u003cp\u003eTable S2\u003c/p\u003e","description":"","filename":"S4.csv","url":"https://assets-eu.researchsquare.com/files/rs-3925941/v1/08d3e21cd3c3787afba6e8d9.csv"}],"financialInterests":"No competing interests reported.","formattedTitle":"Potential causal relationship between body mass index (BMI) and acute pancreatitis: a Mendelian randomization study","fulltext":[{"header":"Full Text","content":"\u003cp\u003eAcute pancreatitis (AP) is a common inflammatory disease caused by multiple pathogenic factors, with clinical manifestations mostly including nausea, fever, and severe abdominal pain\u003csup\u003e[\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. The etiology of AP is complex, the condition develops rapidly, and treatment is difficult. Patients with more severe conditions can quickly develop into severe acute pancreatitis, with a higher mortality rate. The most common causes are biliary diseases, hyperlipidemia, and alcohol consumption. Among them, the occurrence of hyperlipidemic pancreatitis (HLP) is not related to serum cholesterol levels, but closely related to a significant increase in serum triglyceride (TG) levels, hence it is also known as hypertriglyceridemic pancreatitis (HTGP)\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. In recent years, the incidence rate of HTGP is on the rise, and often leads to more serious clinical processes. A large sample study in Jiangxi Province, China analyzed the incidence trend of AP from 2005 to 2012, showing that hyperglycemia and blood lipids have surpassed alcohol as the second leading cause of biliary diseases (14.3%), and the mortality rate of severe HTPG is significantly higher than that of severe biliary pancreatitis\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. A large-scale study in developed regions of China shows that the proportion of hyperlipidemia in the etiology of AP has jumped to 25.6%\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. In addition, HTGP mostly occurs in young men, especially in patients with obesity, alcoholism and diabetes\u003csup\u003e[\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. Although people's cognition of AP has been greatly improved in recent years, its incidence rate is still rising year by year, especially the case fatality rate of severe acute pancreatitis has reached as high as 20% -50%\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. Active research on the correct methods of prevention and early assessment of AP can reduce the incidence rate of AP and the mortality rate after turning to severe acute pancreatitis\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. In recent years, the incidence of obesity and hyperlipidemia in the Chinese population has been increasing year by year. Numerous studies have found that obesity is one of the risk factors for AP. There are also studies indicating that obesity can be one of the indicators for evaluating the severity of AP\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. As BMI serves as a standard for assessing obesity, it also has the potential to serve as an indicator for predicting the risk of developing AP.\u003c/p\u003e\u003cp\u003eMendelian randomization (MR) is a new method of inferring causal effects that has emerged in recent years\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. This method uses genetic variation as the instrumental variable and explores the causal relationship between exposure and outcomes based on the strong correlation between instrumental variables and exposure factors\u003csup\u003e[\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. This study will explore the causal relationship between BMI and AP through two sample Mendelian randomization methods.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003eThis study adopts a two sample Mendelian randomization method, using single nucleotide polymorphism (SNP) loci related to BMI levels as instrumental variables to explore the causal relationship between BMI and the onset of AP. In this MR study, the selected working variables need to meet the following three assumptions: ① Genetic variation is related to BMI level; ② Genetic variation must not be associated with any confounding factors affecting AP, nor with confounding factors on the exposure outcome pathway; ③ Genetic variation only affects outcomes through BMI (Figure 1).\u003c/p\u003e\n\u003cp\u003e1.1 Selection of data sources and instrumental variables\u003c/p\u003e\n\u003cp\u003eMR Base Database（\u0026nbsp;http://www.mrbase.org/\u0026nbsp;）is a large collection of statistical data containing hundreds of genome-wide association studies (GWAS). We searched using the publicly available GWAS meta-analysis summary statistical dataset and analyzed BMI data of European descent (n=454884) as exposure factors. We screened GWAS data and included SNPs significantly correlated with BMI. Based on the results of BMI meta-analysis, SNPs with a correlation of P\u0026lt;5E-08 were included as instrumental variables in this study; And in order to avoid the impact of linkage imbalance (LD) in SNP on the analysis results, the parameter r\u003csup\u003e2\u003c/sup\u003e threshold is set to 0.001 and the distance is set to 10000 kb. In order to ensure a strong correlation between instrumental variables and endogenous variables and avoid weak instrumental bias, we calculated r\u003csup\u003e2\u003c/sup\u003e for each SNP, representing the proportion of variance explained by the instrumental variable SNP; The F-statistic is used to evaluate the strength of the instrumental variable based on the P-value threshold of 5.00E-08 (whole genome significance). Genetic variations related to BMI were screened as instrumental variables (IV), and then two sample MR analysis was performed. Finally, 241 SNPs were included as IVs. We also used publicly published acute pancreatitis data from the UK Biobank (n=463010)（\u0026nbsp;http://www.finngen.fi/en\u0026nbsp;）As a result. To avoid bias due to population stratification, all the genetic data we selected were sourced from the European population.\u003c/p\u003e\n\u003cp\u003e1.2 Statistical analysis\u003c/p\u003e\n\u003cp\u003eMendelian randomization is the use of genetic variation in non experimental data to estimate the causal relationship between exposure and outcome. This MR study mainly uses the inverse variance weighted method (IVW) to explore the causal relationship between BMI level and AP. We used both the weighted median method (WM) and the Mendelian randomization method (MR Egger) based on Egger regression to perform sensitivity analysis on the statistical results. IVW is considered the standard method for summarizing data in MR. This method uses the Wald ratio method to estimate the causal effects of each included tool SNP, and then performs a weighted summary analysis. The weighted median estimation method only requires that at least 50% of the weights contributed by genetic variation are valid for statistical calculation. MR Egger regression can detect and correct for pleiotropy issues, requiring the included instrumental variables to satisfy the InSIDe hypothesis (Instrument strength independent of direct effect, InSIDE), which assumes that the correlation between tool exposure and tool outcome is independent. A P-value less than 0.05 is considered statistically significant for testing. All MR analyses were conducted on the MR Base platform (application version 1.2.1 e646be [June 27, 2018], R version 3.5.0).\u003c/p\u003e\n\u003cp\u003e1.3 Heterogeneity and Sensitivity Testing\u003c/p\u003e\n\u003cp\u003eWe evaluated the heterogeneity between SNPs using Cochran\u0026apos;s Q-statistic and I\u003csup\u003e2\u003c/sup\u003e statistic, and if P\u0026gt;0.05, it indicates no significant heterogeneity. At the same time, we also excluded and included SNPs one by one by leaving one method to observe whether it had an impact on the analysis results, and drew a forest map. If a certain SNP was excluded and P\u0026gt;0.05 was obtained, it is considered that SNPs will not have a significant impact on the results. In terms of pleiotropy, we used the intercept term of MR Egger regression and Mendelian random pleiotropy residual test to include the level pleiotropy of SNP. In MR Egger regression, if the intercept tends to 0, it can be considered that there is no pleiotropy in the results.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e2.1 Mendelian Randomization Analysis Tool Variable Screening Results\u003c/p\u003e\n\u003cp\u003eWe screened 241 independent SNPs from BMI\u0026apos;s GWAS data as IVs ( Table S1、S2). These are significantly correlated with BMI throughout the entire genome (Table 1, Figure 1). The results showed that 241 SNPs were positively correlated with AP, and the results were statistically significant (Table 1, Table S1). The exposure variance (r\u003csup\u003e2\u003c/sup\u003e statistic) of 2.2E-05 is caused by genetic variation in IVs. A P-value of 5.00E-08 corresponds to a F-statistic value of\u0026gt;30 for each variable, and when the F-value is less than 10, it is defined as weak IVs. Therefore, weak instrument bias can be ignored.\u003c/p\u003e\n\u003cp\u003e2.2 Mendelian Randomization Analysis Results\u003c/p\u003e\n\u003cp\u003eThe IVW method results confirm a causal relationship between BMI levels and AP（\u0026nbsp;\u0026beta;= 0.001969, SE=0.0004278, P=0.000004189; Table 1, Figures 1 and 2). The results of the weighted median method also indicate a causal relationship between BMI and AP（\u0026nbsp;\u0026beta;= 0.001901, SE=0.0008264, P=0.02142; Table 1, Figures 1 and 2). The intercept represents the average pleiotropic effect of genetic variation (the direct impact of variation on the average outcome). If the intercept approaches 0, it can be considered that there is no horizontal pleiotropy.\u003c/p\u003e\n\u003cp\u003eThe MR Egger regression results showed that directional pleiotropy had almost no impact on the results (intercept=4.6E-07; P=0.983). MR Egger analysis results showed no causal correlation between BMI and AP（\u0026nbsp;\u0026beta;= 0.001943, SE=0.001326, P=0.1442; Table 1, Figures 1 and 2). In summary, the results of MR analysis are likely to support the potential causal relationship between BMI and AP. And due to three methods \u0026beta; The values are all greater than 0, indicating that BMI is likely to be an unfavorable factor affecting the onset of AP.\u003c/p\u003e\n\u003cp\u003e2.3 Heterogeneity and Sensitivity Analysis\u003c/p\u003e\n\u003cp\u003eIn terms of sensitivity analysis, Cochran\u0026apos;s Q test showed no significant heterogeneity among the instrumental variables (P\u0026gt;0.05). The retention method showed no significant difference in statistical results after excluding the included SNPs (P\u0026gt;0.05). The symmetry of the funnel plot and the intercept of each group in the MR Egger regression test results are equal to or close to 0, and P\u0026gt;0.05 indicates that the results do not have directional level pleiotropy, which also indicates that the MR analysis results are almost unbiased. The specific results are shown in Table 1, Figure 3, and Figure 4.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eExploring the causal relationship between exposure factors and AP is crucial for disease prevention, early diagnosis, and optimization of treatment plans. Although randomized controlled trials (RCTs) are the \u0026quot;gold standard\u0026quot; for clarifying causal relationships, completing RCTs requires a significant amount of manpower and material resources, and in most cases, it is also ethical\u003csup\u003e[17]\u003c/sup\u003e. Therefore, we can use Mendelian randomization methods. Mendelian randomization revolves around Mendelian\u0026apos;s second law, where parents with two or more pairs of relative traits hybridize to produce gametes in their offspring\u003csup\u003e[18]\u003c/sup\u003e. At the same time as allele segregation, genes on non homologous chromosomes exhibit free combination. The statistical essence is to determine causal relationships by screening and utilizing instrumental variables\u003csup\u003e[19,20]\u003c/sup\u003e. In this study, we analyzed whether there is a causal correlation between BMI and AP through Mendelian randomization research, and found that there is a causal correlation between BMI level and the risk of AP occurrence, which is consistent in the IVW and WM methods. And a high BMI level is a risk factor that promotes the occurrence of AP. The data included in this study were all from large-scale GWAS studies, and no significant heterogeneity or pleiotropy was found between instrumental variables, which ensured the stability and credibility of the results.\u003c/p\u003e\n\u003cp\u003eAcute pancreatitis is caused by the activation of a large amount of trypsin in patients, which promotes the digestion of pancreatic tissue and ultimately leads to necrosis of the pancreatic region and adjacent tissues\u003csup\u003e[21]\u003c/sup\u003e. For patients with organ failure and other concurrent symptoms, it is called severe acute pancreatitis. Due to the rapid development of the condition and high mortality rate of these patients. Therefore, how to evaluate the severity of its condition has become one of the hot topics in clinical research. It is currently believed that the presence of hyperglycemia at admission is associated with poor prognosis in patients with acute pancreatitis, and obesity is also considered a risk factor for severe acute pancreatitis (SAP)\u003csup\u003e[22-25]\u003c/sup\u003e. Therefore, measuring BMI can become an important indicator for predicting the risk of AP occurrence and evaluating patient prognosis. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTwo prospective cohort studies from Copenhagen included 118085 participants and measured their baseline BMI values. The results showed that the higher the BMI, the higher the risk of acute pancreatitis\u003csup\u003e[26]\u003c/sup\u003e. Compared to individuals with a BMI of 18.5-24.9, the risk ratio for subjects with a BMI of 25-29.9 was 2.1 (1.6-2.9), and the risk ratio for subjects with a BMI\u0026gt;35 was 2.8 (1.8-4.3). In the age and gender adjusted models, the correlation between triglyceride mediated BMI and the risk of acute pancreatitis was 29% (95% CI: 12-46%, P=0.001), while in the multivariate adjusted model it was 22%. This indicates that higher BMI is associated with a higher risk of acute pancreatitis in the general population, partially mediated by higher triglycerides. According to literature reports, since 2000, the number of inpatients in the United States due to pathological obesity has increased by about 314%, and the incidence rate of acute pancreatitis (AP) closely related to obesity has also shown an increasing trend\u003csup\u003e[27]\u003c/sup\u003e. In 2009, there were more than 274000 inpatients in the United States due to AP, more than twice as many as in 1988. The rise in the incidence rate of AP almost coincides with the global obesity epidemic, suggesting that obesity may be related to the incidence of AP. Another prospective cohort study involving 117531 Danes also explored the correlation between BMI and the onset of acute pancreatitis\u003csup\u003e[28]\u003c/sup\u003e. Based on BMI levels, participants were divided into five groups: Group 1 (underweight group): BMI\u0026lt;18.5kg/m\u003csup\u003e2\u003c/sup\u003e, Group 2 (healthy weight group, also control group): 18.5kg/m\u003csup\u003e2\u003c/sup\u003e\u0026le; BMI \u0026le; 24.9kg/m\u003csup\u003e2\u003c/sup\u003e, Group 3 (super recombination): 25kg/m\u003csup\u003e2\u003c/sup\u003e\u0026le; BMI \u0026le; 29.9kg/m\u003csup\u003e2\u003c/sup\u003e, Group 4 (Grade 1 obesity group): 30kg/m\u003csup\u003e2\u003c/sup\u003e\u0026le; BMI \u0026le; 34.9kg/m\u003csup\u003e2\u003c/sup\u003e, Group 5 (Grade 2 and 3 obesity groups): BMI \u0026ge; 35kg/m\u003csup\u003e2\u003c/sup\u003e. The blood lipid measurement results of participants, as well as other characteristic data such as smoking, drinking, exercise, and medication use, were collected by the researchers. Participants with a history of acute pancreatitis were not included in this study. The research results showed that during a median follow-up period of 8 years, 458 out of 117531 participants developed acute pancreatitis. And researchers found that a BMI of 22kg/m\u003csup\u003e2\u003c/sup\u003eis a node, and when BMI \u0026ge; 22kg/m\u003csup\u003e2\u003c/sup\u003e, the risk of acute pancreatitis in participants increases correspondingly with the increase of BMI level. And after adjusting for multiple variables, the risk of acute pancreatitis was 1.4 times (HR 1.4, 95% CI 1.1-1.8), 2.1 times (HR 2.1, 95% CI 1.6-2.9), and 2.8 times (HR 2.8, 95% CI 1.8-4.3) higher in the overweight group (25-29.9kg/m\u003csup\u003e2\u003c/sup\u003e), grade 1 obesity group (30-34.9kg/m\u003csup\u003e2\u003c/sup\u003e), and grade 2 and 3 obesity group (\u0026gt;35kg/m\u003csup\u003e2\u003c/sup\u003e) compared to the healthy weight group, respectively, with a trend P-value of 2X10\u003csup\u003e-8\u003c/sup\u003e. The research team speculates that high levels of triglycerides are significantly associated with an increased risk of acute pancreatitis. For every 1mmol/L increase in triglycerides, the risk of acute pancreatitis increases by about 12%, and the effect of triglycerides on the increased risk of acute pancreatitis is more significant in women compared to men (P=0.021). In addition, the study also found that smoking is an independent risk factor for acute pancreatitis, with smoking patients having a 36% increased disease risk compared to non-smokers. The mechanism is not yet fully understood, possibly due to the toxicity of free fatty acids produced during the metabolic decomposition of triglycerides. In vitro experiments have confirmed that unsaturated fatty acids have pro-inflammatory effects, release intracellular calcium, interfere with mitochondrial function, and lead to cell necrosis, but saturated fatty acids do not\u003csup\u003e[29]\u003c/sup\u003e. In the case of hypertriglyceridemia, the increase of macromolecular substances such as chylomicrons and extremely low density lipoprotein may block pancreatic capillaries, alter the structure of acinar cells, and ultimately trigger the release of pancreatic enzyme and lipase, leading to related symptoms of acute pancreatitis. And in response to the issue of high triglyceride induced acute pancreatitis, there are currently studies that have confirmed that insulin significantly reduces serum TG levels by promoting the expression of LPL gene mRNA, activating lipoprotein esterase, and accelerating the degradation of chylomicrons in patients with high triglyceride induced acute pancreatitis\u003csup\u003e[30]\u003c/sup\u003e; The overall prognosis can be improved by improving glucose metabolism disorders and reducing the free radicals produced by glucose metabolism disorders in such patients. For some patients, when diagnosed with acute pancreatitis, the levels of triglycerides in their bodies are tested to be higher than 1000mg/dL, and the absolute majority of them have a good prognosis after intensive insulin treatment, especially when the triglyceride levels in these patients decrease to below 500mg/dL after 3-5 insulin treatment, the clinical symptoms of the patients will significantly improve\u003csup\u003e[31-33]\u003c/sup\u003e. Therefore, when the level of triglycerides is below 500mg/dL, insulin treatment can be stopped. In order to prevent the recurrence and progression of acute pancreatitis, long-term testing of triglyceride levels should be carried out to maintain them below 200mg/dL\u003csup\u003e[34]\u003c/sup\u003e. In summary, prospective studies in both Europe and the Americas have shown a correlation between higher BMI and the risk of developing AP, but a study from Sweden concluded that BMI is not associated with the risk of developing AP\u003csup\u003e[35]\u003c/sup\u003e. The research results of this article are consistent with most prospective research conclusions.\u003c/p\u003e\n\u003cp\u003eThis article also has certain limitations. For example, we only found that a high BMI value increases the risk of AP occurrence, but failed to determine the specific trend of AP occurrence risk within different BMI levels. Therefore, studying the impact of different BMI value ranges on the risk of AP occurrence may have more clinical guiding significance. In the future, large-scale, standardized clinical trials and related MR analysis are still needed to conduct more in-depth research on the impact and clinical significance of BMI levels within different ranges on the risk of AP occurrence and prognosis.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflicts of Interest:\u003c/h2\u003e \u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThis study did not receive external funding.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eSiqi Yang is responsible for analyzing article data, using software, and writing articles;Qiao Chi is responsible for reviewing articles and analyzing data results;Waxing Wang is responsible for controlling the quality of articles and proofreading them.\u003c/p\u003e\u003ch2\u003eData Availability Statement:\u003c/h2\u003e \u003cp\u003eThe data that support the findings of this study are openly available in Finnish database at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.finngen.fi/en\u003c/span\u003e\u003cspan address=\"http://www.finngen.fi/en\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e and GWAS database at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gwas.mrcieu.ac.uk\u003c/span\u003e\u003cspan address=\"https://gwas.mrcieu.ac.uk\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003ePeter AB, Thomas LB, Christos D, Hein GG, Colin DJ, Michael GS, et al. Classification of acute pancreatitis\u0026mdash;2012: revision of the Atlanta classification and definitions by international consensus. Gut (2013) 62:102\u0026ndash;111. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/gutjnl-2012-302779\u003c/span\u003e\u003cspan address=\"10.1136/gutjnl-2012-302779\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRebecca AN, Daniel KB, Ruth EP, Gary LB. Increased Risk of Acute Pancreatitis and Biliary Disease Observed in Patients With Type 2 Diabetes: A retrospective cohort study. Diabetes Care (2009) 32:834. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2337/dc08-1755\u003c/span\u003e\u003cspan address=\"10.2337/dc08-1755\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaxim SP, Satyanarayan S, Mandira C, Anthony RJP, John AW. Organ failure and infection of pancreatic necrosis as determinants of mortality in patients with acute pancreatitis. Gastroenterology (2010) 139:813\u0026ndash;820. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1053/j.gastro.2010.06.010\u003c/span\u003e\u003cspan address=\"10.1053/j.gastro.2010.06.010\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWayne T, Udayakumar N, Luis R, Joseph BP, Andres G. Hypertriglyceridemic pancreatitis: presentation and management. Am J Gastroenterol (2009) 104:984\u0026ndash;991. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/ajg.2009.27\u003c/span\u003e\u003cspan address=\"10.1038/ajg.2009.27\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHe WH, Lu NH. Emergent Triglyceride-Lowering Therapy for Hypertriglyceridemic Pancreatitis. Hepatogastroenterology (2015) 62:429\u0026ndash;434. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/MCG.0000000000000552\u003c/span\u003e\u003cspan address=\"10.1097/MCG.0000000000000552\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLu ZH, Chen YL, Wu YS, Lin YJ, Yang N, Wang XL, et al. The role of double filtration plasmapheresis in hypertriglyceridemic pancreatitis: A propensity score matching analysis. J Clin Apher (2020) 35:388\u0026ndash;397. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/jca.21811\u003c/span\u003e\u003cspan address=\"10.1002/jca.21811\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu BJ, He WH, He C, Li NS, Li JR, Zhu Y, et al. Low-Molecular-Weight Heparin Combined With Insulin Versus Insulin Alone in the Treatment of Hypertriglyceridemic Pancreatitis (LIHTGP Trial): Study Protocol for a Multicenter, Prospective, Single-Blind, Randomized Controlled Trial. Pancreas (2020) 49:1383\u0026ndash;1387. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/MPA.0000000000001784\u003c/span\u003e\u003cspan address=\"10.1097/MPA.0000000000001784\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJakob J, Jadranka BP, Karmen R, Rafael P. Factors Affecting Outcome in Acute Hypertriglyceridemic Pancreatitis Treated with Plasma Exchange: An Observational Cohort Study. Plos One (2014) 9:e102748. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pone.0102748\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0102748\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen WJ, Sun XF, Zhang RX, Xu MJ, Dou TH, Zhang XB, et al. Hypertriglyceridemic acute pancreatitis in emergency department: Typical clinical features and genetic variants. J Dig Dis (2017) 18:359\u0026ndash;368. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/1751-2980.12490\u003c/span\u003e\u003cspan address=\"10.1111/1751-2980.12490\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLu ZH, Chen YL, Wu YS, Lin YJ, Yang N, Wang XL, et al. The role of double filtration plasmapheresis in hypertriglyceridemic pancreatitis: A propensity score matching analysis. J Clin Apher (2020) 35:388\u0026ndash;397. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/jca.21811\u003c/span\u003e\u003cspan address=\"10.1002/jca.21811\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMichael AM, Howard AR, Mark DG. Acute Pancreatitis: A Review. JAMA (2021) 325:382\u0026ndash;390. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1001/jama.2020.20317\u003c/span\u003e\u003cspan address=\"10.1001/jama.2020.20317\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGreenberg JA, Hsu J, Bawazeer M, Marshall J, Friedrich JO, Nathens A, et al. Clinical practice guideline: management of acute pancreatitis. Can J Surg (2016) 59:128\u0026ndash;140. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1503/cjs.015015\u003c/span\u003e\u003cspan address=\"10.1503/cjs.015015\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShah E, George DS. Mendelian randomization: can genetic epidemiology help redress the failures of observational epidemiology? Hum Genet (2008) 123:15\u0026ndash;33. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00439-007-0448-6\u003c/span\u003e\u003cspan address=\"10.1007/s00439-007-0448-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDuncan CT, David VC. Commentary: The concept of 'Mendelian Randomization'. Int J Epidemiol (2004) 33:21\u0026ndash;25. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/ije/dyh048\u003c/span\u003e\u003cspan address=\"10.1093/ije/dyh048\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCosetta M, John RT, Martin DT, Keith RA. An integrated approach to the meta-analysis of genetic association studies using Mendelian randomization. Am J Epidemiol (2004) 160:445\u0026ndash;452. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/aje/kwh228\u003c/span\u003e\u003cspan address=\"10.1093/aje/kwh228\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSmith GD, Ebrahim S. Mendelian randomization: prospects, potentials, and limitations. Int J Epidemiol (2004) 33:30\u0026ndash;42. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/ije/dyh132\u003c/span\u003e\u003cspan address=\"10.1093/ije/dyh132\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFarkas G. Inflammatory mediators in acute pancreatitis (theoretical considerations). Orv Hetil (1995) 136:1819\u0026ndash;1822. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3748/wjg.v10.i19.2898\u003c/span\u003e\u003cspan address=\"10.3748/wjg.v10.i19.2898\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCaroline LR, George DS. Two-step epigenetic Mendelian randomization: a strategy for establishing the causal role of epigenetic processes in pathways to disease. Int J Epidemiol (2012) 41:161\u0026ndash;76. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/ije/dyr233\u003c/span\u003e\u003cspan address=\"10.1093/ije/dyr233\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMurielle B, Valentin R. Usefulness of Mendelian Randomization in Observational Epidemiology. Int J Environ Res Public Health (2010) 7:711\u0026ndash;728. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/ijerph7030711\u003c/span\u003e\u003cspan address=\"10.3390/ijerph7030711\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHe WJ, John C, Elizabeth AW, Jason FO, Sunil KA. Mendelian randomization: potential use of genetics to enable causal inferences regarding HIV-associated biomarkers and outcomes. Curr Opin HIV AIDS (2010) 5:549\u0026ndash;559. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/COH.0b013e32833f2087\u003c/span\u003e\u003cspan address=\"10.1097/COH.0b013e32833f2087\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJolanta J, Joanna B, Piotr P, Romana T, Jerzy S, Ryszard S, et al. Leptin protects the pancreas from damage induced by caerulein overstimulation by modulating cytokine production. Pancreatol (2002) 2:89\u0026ndash;99. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1159/000055897\u003c/span\u003e\u003cspan address=\"10.1159/000055897\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJiang K, Huang W, Yang XN, Xia Q. Present and future of prophylactic antibiotics for severe acute pancreatitis. World J Gastroenterol (2012) 18:279\u0026ndash;284. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3748/wjg.v18.i3.279\u003c/span\u003e\u003cspan address=\"10.3748/wjg.v18.i3.279\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarta P, Grazyna R, Janusz M, Stanisław O, Mariusz F, Jerzy W, et al. The results of severe acute pancreatitis treatment with continuous regional arterial infusion of protease inhibitor and antibiotic: a randomized controlled study. Pancreas (2010) 39:863\u0026ndash;867. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/MPA.0b013e3181d37239\u003c/span\u003e\u003cspan address=\"10.1097/MPA.0b013e3181d37239\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu XM, Ji KQ, Wang HY, Li GF, Zang B, Chen WM. Total enteral nutrition in prevention of pancreatic necrotic infection in severe acute pancreatitis. Pancreas (2010) 39:248. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/MPA.0b013e3181bd6370\u003c/span\u003e\u003cspan address=\"10.1097/MPA.0b013e3181bd6370\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVege SS, Baron TH. Management of pancreatic necrosis in severe acute pancreatitis. Clin Gastroenterol Hepatol (2005) 3:192\u0026ndash;196. DOI: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/s1542-3565(04)00668-8\u003c/span\u003e\u003cspan address=\"10.1016/s1542-3565(04)00668-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAhmet T, Abdullah B, Mehmet HT, Ercan G, Abdurrahim D, İbrahim K, et al. The Predict and Prognosis of Ghrelin and BMI in Acute Pancreatitis. Int J Surg (2014) 12:1310\u0026ndash;1313. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ijsu.2014.10.040\u003c/span\u003e\u003cspan address=\"10.1016/j.ijsu.2014.10.040\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGeorgios IP, Dionysios JP, Haritha A, Adam S, David CW. Obesity increases the severity of acute pancreatitis: performance of APACHE-O score and correlation with the inflammatory response. Pancreatol (2006) 6:279\u0026ndash;285. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1159/000092689\u003c/span\u003e\u003cspan address=\"10.1159/000092689\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBiswajit K, Bara EK, Vijay PS. Obesity and acute pancreatitis. Curr Opin Gastroenterol (2017) 33:374\u0026ndash;382. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/MOG.0000000000000386\u003c/span\u003e\u003cspan address=\"10.1097/MOG.0000000000000386\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStephen JP, Roberta AG. Calcium, mitochondria and the initiation of acute pancreatitis. Pancreatology (2022) 22:838\u0026ndash;845. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.pan.2022.07.011\u003c/span\u003e\u003cspan address=\"10.1016/j.pan.2022.07.011\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHan P, Wei GH, Cai K, Xiang X, Deng WP, Li YB, et al. Identification and functional characterization of mutations in LPL gene causing severe hypertriglyceridaemia and acute pancreatitis. J Cell Mol Med (2020) 24:1286\u0026ndash;1299. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/jcmm.14768\u003c/span\u003e\u003cspan address=\"10.1111/jcmm.14768\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRefaat AH, Tiffany D. Prebiotics in Immuno-Modulation for Treatment of Acute Pancreatitis. World J Gastroenterol (2014) 20:16101\u0026ndash;16105. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3748/wjg.v20.i43.16101\u003c/span\u003e\u003cspan address=\"10.3748/wjg.v20.i43.16101\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShen JQ, Shen J, Wang XP. Expression of insulin-like growth factor binding protein-4 (IGFBP-4) in acute pancreatitis induced by l-arginine in mice. Acta Histochem (2012) 114:379\u0026ndash;385. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.acthis.2011.07.008\u003c/span\u003e\u003cspan address=\"10.1016/j.acthis.2011.07.008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMadan RA, Naba RM, Shobhit G, Manoj S. Acute pancreatitis owing to very high triglyceride levels treated with insulin and heparin infusion. Bmj Case Rep (2013) 22:bcr2013008550. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/bcr-2013-008550\u003c/span\u003e\u003cspan address=\"10.1136/bcr-2013-008550\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu B, He W, He C. Low-Molecular\u0026ndash;Weight Heparin Combined With Insulin Versus Insulin Alone in the Treatment of Hypertriglyceridemic Pancreatitis (LIHTGP Trial): Study Protocol for a Multicenter, Prospective, Single-Blind, Randomized Controlled Trial. Pancreas (2021) 50:e40. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/MPA.0000000000001784\u003c/span\u003e\u003cspan address=\"10.1097/MPA.0000000000001784\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKerstin BB, Anders S, Gunnar S, Bengt EW. Glibenclamide and obesity may be risk-factors for acute pancreatitis. Diabetes Care (2002) 25:298\u0026ndash;302. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2337/diacare.25.2.298\u003c/span\u003e\u003cspan address=\"10.2337/diacare.25.2.298\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":" \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cdiv class=\"SimplePara\"\u003eEvaluation of the causal relationship between BMI and the risk of acute pancreatitis using each MR analysis method\u003c/div\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eMR method\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eNumber of SNPs\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003eBeta\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003eSE\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e95% confidence interval\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003eAssociation P-value\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003eCochran Q statistic\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cdiv class=\"SimplePara\"\u003eI2\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cdiv class=\"SimplePara\"\u003eHeterogeneity P-value\u003c/div\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eInverse-variance weighted\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e241\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.001969\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.0004278\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.00187055\u0026ndash;0.00206745\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.000004189\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e220.9\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.007\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.8068\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eMR Egger\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e241\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.001943\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.001326\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.00184585\u0026ndash;0.00204015\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.1442\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e220.9\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.004\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.7939\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eWeighted median\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e241\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.001901\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.0008264\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.00180595\u0026ndash;0.00199605\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.02142\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e/\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cdiv class=\"SimplePara\"\u003e/\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cdiv class=\"SimplePara\"\u003e/\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003cbr/\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"body mass index, acute pancreatitis, Mendelian randomization, correlation, risk of onset","lastPublishedDoi":"10.21203/rs.3.rs-3925941/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3925941/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective:\u003c/strong\u003e To investigate the causal relationship between body mass index (BMI) levels and acute pancreatitis using the two sample Mendelian randomization method (MR).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethod:\u003c/strong\u003e Analyze the aggregated data from the gene wide association study (GWAS). The GWAS summary data used were all sourced from the European population. Using single nucleotide polymorphisms significantly correlated with body mass index levels as instrumental variables, a two sample Mendelian randomization analysis was performed using inverse variance weighted (IVW), MR Egger regression, and weighted median (WM) methods, respectively, to evaluate the causal effect of body mass index and acute pancreatitis using odds ratio (Oddsratio, OR). The body mass index data as an exposure factor was obtained from individuals of European ancestry in the UK Biobank (n=454884). We also used GWAS's publicly aggregated statistical dataset for self-reported non cancer disease codes: acute pancreatitis data included in the UK Biobank (n=463010)（ http://www.nealelab.is/uk-biobank ））as the ending.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResult:\u003c/strong\u003e The IVW results showed that BMI was associated with acute pancreatitis（ β= 0.001969, se=0.0004278, P=0.000004189) showed a significant correlation, and the weighted median results were consistent with the IVW result（ β= 0.001901, se=0.0008264, P=0.02142), also confirming the causal relationship between BMI and acute pancreatitis. The MR Egger regression results confirm that directional pleiotropy is unlikely to bias the results (intercept=4.6E-07; P=0.983), but there is no causal relationship between BMI and acute pancreatitis（ β= 0.001943, se=0.001326, P=0.1442). Cochran's Q-test and funnel plot indicate no evidence of heterogeneity and asymmetry, indicating the absence of directed pleiotropy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e The results of MR analysis support a causal relationship between body mass index (BMI) and an increased risk of acute pancreatitis.\u003c/p\u003e","manuscriptTitle":"Potential causal relationship between body mass index (BMI) and acute pancreatitis: a Mendelian randomization study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-07 20:03:38","doi":"10.21203/rs.3.rs-3925941/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ff4ca5d2-b153-4e84-a162-e16c0823a7eb","owner":[],"postedDate":"February 7th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-02-16T02:50:15+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-07 20:03:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3925941","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3925941","identity":"rs-3925941","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}