Skip to main content
Download PDF

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

BMC Gastroenterology volume 24, Article number: 32 (2024) Cite this article

Abstract

Background

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

Methods

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

Result

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

Conclusion

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

Peer Review reports

Introduction

Acute pancreatitis (AP) is a common gastroenterological condition, with approximately 80% of patients developing mild to moderately severe disease (no organ failure > 48 h) and the rest progressing into severe acute pancreatitis (SAP) [1]. The death rate of SAP is as high as 20%, therefore, early assessment of severity in AP is crucial. Despite the large number of studies exploring early prediction of AP severity [2, 3], no ideal multifactorial scoring system and/or biochemical markers have been identified for early assessment of AP severity [4]. Therefore, early identification of the development of severe AP remains a great challenge.

In clinical studies, the components of metabolic syndrome have been found to be associated with the occurrence and deterioration of AP [5, 6]. In particular, obesity is an independent risk factor for the AP morbidity and mortality [7,8,9,10]. Depending on its location, adipose tissue can be divided into subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT). SAT, accounting for approximately 80% of all adipose tissue, acts as a reservoir for excess lipids. However, once the storage capacity is exceeded, which can only accommodate a limited number of adipocytes with limited expandability, fat begins to accumulate in areas outside the SAT, such as the liver, heart, skeletal muscles, and other sites [11, 12]. Numerous studies have shown that VAT, associated with the occurrence and development of AP [13,14,15], is a key site of inflammation and responsible for driving the systemic inflammatory response and exacerbating AP [16, 17], thus serving as an important prognostic indicator of AP severity. Being highly metabolically active, VAT can continuously release adipokines such as resistin, leptin, adiponectin, and visfatin into the portal circulation [18], which may involve in the development and progression of AP by modulating oxidative stress and inflammatory responses and influencing the severity of AP. Furthermore, resistin, leptin, adiponectin, and visfatin are well-known biomarkers for Nonalcoholic Fatty Liver Disease (NAFLD), which is a strong risk factor for AP and SAP.

Resistin has been found to increase the production of pro-inflammatory cytokines such as TNF-α, IL-1β, and IL-6 in mononuclear cells and macrophages [19, 20]. Additionally, it stimulates the production of cell adhesion molecules, including vascular cellular adhesion molecule-1 (VCAM-1), intercellular adhesion molecule-1 (ICAM-1), and monocyte chemoattractant protein (MCP)-1, as well as chemokine (C-C motif) ligand 2 (CCL 2), which contribute to chemotaxis and leukocyte recruitment to sites of inflammation [21, 22]. Leptin, which is mainly secreted by adipocytes, is a potent chemoattractant for immune cells, causing monocytes and macrophages to accumulate towards adipose tissue, and promoting increased expression of the inflammatory cytokines IL-6 and tumor necrosis factor (TNF) as well as toll-like receptor 4 (TLR4) [23]. At the same time, leptin is required for T-cell development and promotes the production of pro-inflammatory cytokines in CD4[+] T cells [24,25,26]. Adiponectin, a hormone mainly produced by white adipose tissue, can inhibit M1 macrophage activation [27, 28], exert anti-inflammatory effects by regulating JmJC family histone demethylase 3, which contributes to M2 polarization [29], and inhibit macrophage infiltration [30]. In animal studies, adiponectin-deficient mice exhibited more severe AP than wild-type mice, and adiponectin overexpression reduced the severity of AP [31]. Administration of exogenous recombinant adiponectin to AP mice significantly reduced NF-kB activity, cytokine levels, and tissue damage [32]. Visfatin has nicotinamide phosphoribose transferase (Nampt) activity, the rate-limiting enzyme of the nicotinamide adenine dinucleotide (NAD) salvage synthesis pathway, and macrophages rely on the NAD salvage pathway to meet their energy requirements and maintain their pro-inflammatory phenotype. Visfatin also promotes the release of pro-inflammatory cytokines IL-1β, IL-6, and TNF-α from peripheral monocytes [33,34,35].

Despite many studies that have explored the relationship between adipokines and SAP, the findings have been inconsistent. Furthermore, even though a meta-analysis of the relationship between adipokines and SAP has recently been published [36], it only examined the statistical correlation between resistin and SAP, without addressing the correlation between other adipokines and SAP. Therefore, we performed this meta-analysis, involving such adipokines as resistin, leptin, adiponectin, and visfatin, to explore the correlation between adipokines and SAP.

Method

This study was performed in accordance with the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines.

Search strategy

We conducted a systematic literature search on Embase, Cochrane library, PubMed and Web of Science, using the following keywords: (“adipokines”, “resistin”, “leptin”, “visfatin” or “adiponectin”) AND (“acute pancreatitis”) and MeSH/Emtree terms as well (Table S1). The deadline for the search was July 20, 2023. In addition, we checked the references of the screened literature to identify any additional relevant studies.

Study selection

Inclusion criteria: (1) study subjects with a confirmed diagnosis of AP were included; (2) the severity of the AP was assessed; (3) the concentration of resistin, leptin, endolipin or lipocalin in peripheral blood was measured; (4) complete data calculation metrics were available: including the mean of the concentrations of resistin, leptin, visfatin or adiponectin with corresponding standard deviations (SD) or 95% confidence intervals (CI); (5) studies republished after additional data in the literature on the same topic, using the most recent study data.

Exclusion criteria: (1) duplicate articles; (2) reviews, meta-analyses, editorials, and letters; (3) animal studies or in vitro experiments; (4) articles whose data were unavailable; (5) studies that were subgroup analyses of included multicenter studies.

Both the study selection and exclusion procedures described above were conducted by two independent investigators (Xuehua Yu and Ning Zhang). Once disagreements occur, a third independent reviewer (Jing Wu) was invited to make the final decision.

Data extraction and quality assessment

Data were extracted and cross-checked independently by two authors (Xuehua Yu and Ning Zhang) using a pre-developed data extraction form, and in case of disagreement, they were referred to a third investigator (Yunhong Zhao) for verification. Extractions included: first author, year of publication, country, types of adipokines, the time of the blood test, assay method, AP diagnostic criteria, sample size, sample characteristics, etiology, adipokine concentration (mean, SD), and fund.

To evaluate the risk of bias and quality of all included studies, we used the Quality Assessment of Diagnostic Accuracy Studies tool (QUADAS) [26], which was adapted to the studies included in this meta-analysis. All assessments were performed by two independent investigators (Xuehua Yu and Ning Zhang), and any disputes were resolved through consultation or discussion with a third party (Chengjiang Liu).

Statistical analysis

Continuous outcomes measured on the same scale were expressed as a mean value and standard deviation and were analyzed by using standardized mean difference (SMD). Statistical analyses of heterogeneity were conducted using the chi-squared Q test and the I-square (I2) statistic. P < 0.10 and I2 > 50% were considered statistically significant heterogeneity thresholds. Calculation of the pooled SMD was performed using a random effects model. Moreover, subgroup and sensitivity analyses were used to further explore the sources of heterogeneity. All P-values were 2-tailed, and P < 0.05 (except for tests of heterogeneity) was considered statistically significant. Publication bias was assessed by Egger’s test and Begg’s test.

Results

Literature search and research characteristics

According to a predefined search strategy, we searched PubMed, EMBASE, Web of Science, and Cochrane Library, generating 1266 articles. By strictly following the inclusion and exclusion criteria, 20 articles were finally included, and the specific screening process is shown in Fig. 1.

Fig. 1
figure 1

The PRISMA flow chart of literature screening

Full size image

The main characteristics of the included studies are summarized in Table 1, Table S2, S3 and S4. A total of 1332 AP patients were evaluated in studies conducted in countries (4 in Turkey, 3 in the United States, 3 in China, 2 in Czech Republic, 2 in Germany, 1 in India, 1 in México, 1 in Poland, 1 in Finland, 1 in Saudi Arabia, and 1 in Lithuania). Among the 20 studies, 10 evaluated the predictive effect of resistin on SAP, 8 focused on the predictive effect of leptin on SAP, 7 evaluated the predictive effect of adiponectin on SAP, and 3 investigated the predictive effect of visfatin on SAP. The detailed statistics of each adipocytokine are shown in Table 2. The quality assessment of all included studies that applied the QUADAS risk of bias assessment tool is shown in Table S5.

Table 1 Characteristics of 20 studies included in the meta-analysis (1)
Full size table
Table 2 Circulating levels of resistin, leptin, adiponectin and visfatin in SAP and MAP patients
Full size table

Relationship between adipokines and SAP

A total of 7 of 10 studies showed significantly increased levels of resistin in patients with SAP relative to patients with mild acute pancreatitis (MAP). A total of 275 SAP patients and 541 MAP patients were included in the summary analysis, as shown in Fig. 2A. The pooled analysis showed significantly higher resistin levels in SAP patients as compared to MAP patients (SMD = 0.78, 95% CI:0.37 to 1.19, z = 3.75, P = 0.000). However, statistically significant heterogeneity was observed in these studies (P = 0.000, I2 = 83.9%).

For leptin, 3 out of 8 studies saw significantly higher levels in patients with SAP. A total of 160 SAP patients and 310 MAP patients were analyzed. Leptin levels were not significantly higher in SAP patients than in MAP patients (SMD = 0.30, 95% CI: -0.08 to 0.68, z = 1.53, P = 0.127) (Fig. 2B). Again, significant heterogeneity was observed in the study (P = 0.004, I2 = 66.2%).

A total of 1 out of 7 studies showed significantly lower adiponectin levels in patients with SAP as compared to those with MAP. Pooled analysis showed no significant difference in adiponectin levels between 131 SAP patients and 308 MAP patients (SMD = 0.11, 95% CI: -0.17 to 0.40, z = 0.80, P = 0.425) (Fig. 2C). Significant heterogeneity was found in these 10 studies (P = 0.190, I2 = 31.2%).

Only 3 studies have examined blood visfatin levels in SAP patients and MAP patients. A total of 91 patients with SAP and 126 patients with MAP were analyzed. Visfatin levels were not significantly higher in patients with SAP than in those with MAP (SMD = 1.20, 95% CI: -0.48 to 2.88, z = 1.40, P = 0.162) (Fig. 2D). Again, significant heterogeneity was observed in the study (P = 0.000, I2 = 95.2%).

Fig. 2
figure 2

Forest plots of SMD with 95% CI of peripheral blood levels of resistin (A), leptin (B), adiponectin (C) and visfatin (D) levels between SAP patients and MAP patients

Full size image

Subgroup analysis

According to year of publication, sample size, mean age of patients, and definition of SAP group and MAP group (Table S4), subgroup analysis was performed to explore the impact of these three factors on outcomes as well as to identify potential sources of resistin and leptin heterogeneity.

As shown in Fig S1A, pooled results from the literature published before 2014 and in 2014 and after showed that resistin was predictive of SAP. Pooled results from studies in which the mean age of patients with AP was < 50 years versus age ≥ 50 years also indicated that resistin was a predictor of SAP (Fig S1B). Studies with a sample size of < 100 patients showed significantly higher resistin levels in SAP patients than in MAP patients (SMD = 0.83, 95% CI: 0.42 to 1.24, z = 3.98, P = 0.000, I2 = 64.1%, Fig S1C), but studies having a sample size of ≥ 100 patients showed no statistically significant difference in resistin levels between the two groups (SMD = 0.72, 95% CI: -0.08 to 1.52, z = 1.77, P = 0.076, I2 = 92.6%, Fig S1C). In addition, SAP was defined as persistent organ failure (> 48 h) in 6 studies that tested resistin levels, showing a significant difference between in the MAP group and the SAP group (SMD = 0.80, 95% CI: 0.23 to 1.37, z = 2.73, P = 0.006, I2 = 88.7%, Fig S1D).

Regarding leptin, as shown in Fig S2, different publication years, ages, and definition of SAP and MAP showed no statistically significant difference in leptin levels between the two groups. However, the pooled results of the 7 studies with sample sizes < 100 showed that leptin levels were higher in the SAP group than in the MAP group, and the difference was statistically significant (SMD = 0.40, 95% CI: 0.02 to 0.77, z = 2.07, P = 0.038, I2 = 57.2%, Fig S2C).

There were no statistically significant differences in lipocalin levels between the two groups for different publication years, sample sizes, ages, and definitions of SAP and MAP, as shown in Figure S3.

Sensitivity analysis

Sensitivity analysis was performed whereby each study was excluded in turn to assess the stability of the results and the impact of each study on the pooled SMD was also determined (Fig. 3). It can be seen from Fig. 3A that the studies by Kibar YI et al., Singh AK et al. and Langmead C et al. had the greatest influence on the results regarding resistin. Although these 3 studies were removed, SAP patients showed significantly higher resistance levels than MAP patients (SMD = 0.66, 95% CI: 0.45 to 0.87, z = 6.21, P = 0.000, I2 = 0.0%, Fig S4). As shown in Fig. 3B, the results of the study by Türkoğlu A et al. had the greatest impact on the results regarding leptin, and removal of this study still showed no significant increase in leptin levels in SAP patients compared to MAP patients (SMD = 0.13, 95% CI: -0.15 to 0.41, z = 0.88, P = 0.379, I2 = 23.9%, Fig S5).

Fig. 3
figure 3

The pooled SMD and 95%CI of eligible studies of resistin (A) and leptin (B) through sensitivity analysis

Full size image

Publication bias

For resistin, leptin and lipocalin, symmetry was observed in Begg’s funnel plot (Fig S6), with Egger’s test results (P = 0.444, P = 0.869, P = 0.920, respectively, Fig S7), suggesting no publication bias.

Discussion

The results of this meta-analysis showed that increased resistin levels were associated with SAP, whereas leptin and adiponectin levels were not linked to SAP. Only three of the studies included visfatin, not enough to draw any conclusions.

Resistin is a small protein rich in cysteine, with a molecular weight of either 11 or 12.5 kDa. It was first identified in mice in 2001 as a signal molecule produced by adipocytes, and named resistin because it was thought to be involved in the development of insulin resistance [37]. Resistin belongs to the resistin-like molecule (RELM) family, which includes RELM-α, RELM-β, and RELM-γ [38]. Unlike mice, where resistin is produced by adipocytes, humans mainly express resistin in monocytes and macrophages [39]. Despite only sharing 59% of the same amino acids [40], resistin functions similarly in both humans and rodents, even though they are produced from different sources. Resistin has been identified as a molecule that promotes inflammation and regulates various chronic inflammatory, metabolic, and infectious diseases in humans [41,42,43,44]. It modulates many cellular responses in the host, such as recruiting and activating immune cells, promoting the release of pro-inflammatory cytokines, enhancing interferon (IFN) expression, and promoting the formation of neutrophil extracellular trap networks (NETs) [45,46,47].

The role of resistin in regulating inflammatory pathways has been demonstrated in the context of AP. Resistin increases the levels of calcium in pancreatic follicular cells, as well as the activity of NADPH oxidase, leading to an increase in the production of reactive oxygen species (ROS) within the cells. Additionally, resistin activates the NF-κB pathway, resulting in the expression of pro-inflammatory cytokines such as TNF-α and IL-6 [48, 49]. Jiang et al. demonstrated in a laboratory model of AP induced by cerulein that resistin increases the production of pro-inflammatory cytokines TNF-α and IL-6 via an NF-κB-dependent pathway. However, the increased mRNA expression levels of TNF α and IL 6 induced by resistin can be significantly reduced by using an NF-κB inhibitor [50]. Furthermore, Wang et al. discovered that the severity of SAP lung injury was positively associated with RELMα levels. Moreover, overexpression of RELMα worsened the release of inflammatory cytokines such as interleukin (IL)-1β, IL-6, IL-8, tumor necrosis factor-α, and serum C-reactive protein. This led to an increase in the expression of inflammatory mediators such as phosphorylated (p)-AKT, p-P65, p-P38 mitogen activated protein kinase, p-extracellular regulated kinase, and intracellular adhesion molecule-1, ultimately resulting in lung injury. On the other hand, knocking down RELMα had the opposite effect. It improved the expression of proliferative cellular nuclear antigen, Bcl-2, zonal occludin-1, and Claudin-1 in lung tissue of SAP rats [51]. Furthermore, numerous studies have confirmed the correlation between resistin and the severity of AP. This suggests that resistin may serve as a valuable marker and potential therapeutic target for SAP [52].

Leptin is mainly secreted by fat cells and plays a crucial role in the immune response as an immune modulator [53, 54]. Monocytes treated with leptin increase the production of type 1 cytokines, including IL-1β, IL-6, TNF, and resistin [55, 56]. Adiponectin can inhibit the ROS/NF-κB/NLRP3 inflammatory pathway [57], activate the anti-inflammatory cytokine interleukin-10 (IL-10), and reduce pro-inflammatory cytokines such as interferon-gamma (IFN-γ), IL-6 and TNF-α in human macrophages [58]. The results of this meta-analysis showed that leptin and adiponectin levels were not linked to SAP. However, it is still unclear whether leptin and adiponectin have different effects at different stages of inflammation, or whether an imbalance among leptin, adiponectin and other adipokines may inhibit their regulation of the immune response, or whether there are other possible mechanisms, which need to be confirmed by more studies. Although most studies show that visfatin appears to have pro-inflammatory effects [33,34,35, 59,60,61,62], there are some studies that show the opposite [29, 30, 63]. In response to this seemingly contradictory result, the study by Sayers et al. may give us some insight. They found a possible bimodal effect of extracellular Nampt (eNampt) monomer on the stimulation of insulin secretion by β-cells [64]. Whether this bimodal effect is equally reflected in the stimulatory effect of endolipin on inflammatory factors and the modulation of the inflammatory response, and whether it is this bimodal effect that leads to the unstable prediction of SAP by visfatin, remain to be further explored.

Heterogeneity was observed in our pooled analysis. The resistin results were greatly influenced by two studies, while the leptin results were mainly affected by one study. Several factors such as regions, research samples, and detection reagents can affect the outcomes. Small sample sizes can also lead to accidental findings, making heterogeneity between studies inevitable. However, the stability of the results was confirmed even after removing the heterogeneous studies. Furthermore, sample size and mean age of the patients may be associated with resistin heterogeneity. It has been shown that the adverse effects of obesity appear to be reduced in older populations [65]. Khatua et al. suggested that the different visceral triglyceride saturation status could have varying effects on AP severity, explaining the obesity paradox [66]. Based on the results of the subgroup analysis in this meta-analysis, it appears that the mean age of patients has an effect on adiposity factors and resultantly affects AP severity, which may provide a new thought for the obesity paradox.

There are some limitations to this meta-analysis. Firstly, all studies included were case-control studies with inherent selection, information and confounding biases. Secondly, the sample size was moderate for the included studies and a few of the eligible studies had small sample sizes. Thirdly, changes in testing methods and diagnostic criteria over time may have contributed to the different pooled results between publication years in the subgroup analysis.

In conclusion, the results of this meta-analysis suggest high levels of resistin levels are associated with an increased risk of SAP, indicating resistin may be a potential biomarker. Moreover, serum or plasma samples can be easily obtained for resistin detection, and the assay is uncomplicated and can be performed in many laboratories. Since it is often challenging for a single indicator to accurately predict the severity of AP, it may be possible in the future to predict SAP by testing for the levels of resistin in conjunction with other indicators or by incorporating resistin into a scoring system.

Data availability

All data generated or analyzed during this study are included in this published article and its supplementary information files.

References

  1. Mederos MA, Reber HA, Girgis MD, Acute Pancreatitis. Rev JAMA. 2021;325(4):382–90. https://doi.org/10.1001/jama.2020.20317.

    Article  CAS  Google Scholar 

  2. Mikó A, Vigh É, Mátrai P, Soós A, Garami A, Balaskó M, Czakó L, Mosdósi B, Sarlós P, Erőss B, Tenk J, Rostás I, Hegyi P. Computed Tomography Severity Index vs. other indices in the prediction of severity and mortality in Acute Pancreatitis: a predictive accuracy Meta-analysis. Front Physiol. 2019;10:1002. https://doi.org/10.3389/fphys.2019.01002.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Kui B, Pintér J, Molontay R, Nagy M, Farkas N, Gede N, Vincze Á, Bajor J, Gódi S, Czimmer J, Szabó I, Illés A, Sarlós P, Hágendorn R, Pár G, Papp M, Vitális Z, Kovács G, Fehér E, Földi I, Izbéki F, Gajdán L, Fejes R, Németh BC, Török I, Farkas H, Mickevicius A, Sallinen V, Galeev S, Ramírez-Maldonado E, Párniczky A, Erőss B, Hegyi PJ, Márta K, Váncsa S, Sutton R, Szatmary P, Latawiec D, Halloran C, de-Madaria E, Pando E, Alberti P, Gómez-Jurado MJ, Tantau A, Szentesi A, Hegyi P, Hungarian Pancreatic Study Group.;. EASY-APP: An artificial intelligence model and application for early and easy prediction of severity in acute pancreatitis. Clin Transl Med. 2022;12(6):e842. https://doi.org/10.1002/ctm2.842.

  4. Silva-Vaz P, Abrantes AM, Castelo-Branco M, Gouveia A, Botelho MF, Tralhão JG. Multifactorial scores and biomarkers of prognosis of Acute Pancreatitis: applications to Research and Practice. Int J Mol Sci. 2020;21(1):338. https://doi.org/10.3390/ijms21010338.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Szentesi A, Párniczky A, Vincze Á, Bajor J, Gódi S, Sarlós P, Gede N, Izbéki F, Halász A, Márta K, Dobszai D, Török I, Farkas H, Papp M, Varga M, Hamvas J, Novák J, Mickevicius A, Maldonado ER, Sallinen V, Illés D, Kui B, Erőss B, Czakó L, Takács T, Hegyi P. Multiple hits in Acute Pancreatitis: components of metabolic syndrome synergize each other’s deteriorating effects. Front Physiol. 2019;10:1202. https://doi.org/10.3389/fphys.2019.01202.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Shen Z, Wang X, Zhen Z, Wang Y, Sun P. Metabolic syndrome components and acute pancreatitis: a case-control study in China. BMC Gastroenterol. 2021;21(1):17. https://doi.org/10.1186/s12876-020-01579-3.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. İnce AT, Seven G, Koçhan K, Kiremitçi S, Yıldız K, Şentürk H. The course of acute pancreatitis in patients with different BMI groups. Pancreatology. 2022;22(3):348–55. https://doi.org/10.1016/j.pan.2022.03.009. Epub 2022 Mar 17.

    Article  CAS  PubMed  Google Scholar 

  8. Hansen SEJ, Madsen CM, Varbo A, Nordestgaard BG. Body Mass Index, triglycerides, and risk of Acute Pancreatitis: a Population-based study of 118 000 individuals. J Clin Endocrinol Metab. 2020;105(1):dgz059. https://doi.org/10.1210/clinem/dgz059.

    Article  PubMed  Google Scholar 

  9. Thavamani A, Umapathi KK, Sferra TJ, Sankararaman S. Undernutrition and Obesity Are Associated with adverse clinical outcomes in hospitalized children and adolescents with Acute Pancreatitis. Nutrients. 2020;13(1):43. https://doi.org/10.3390/nu13010043.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Blaszczak AM, Krishna SG, Hart PA, Bradley D, Hsueh W, Lara LF, Hussan H, Hinton A, Conwell DL, Cruz-Monserrate Z. Class III obesity rather than metabolic syndrome impacts clinical outcomes of acute pancreatitis: a propensity score weighted analysis. Pancreatology. 2020;20(7):1287–95. https://doi.org/10.1016/j.pan.2020.08.011.

    Article  PubMed  Google Scholar 

  11. Chait A, den Hartigh LJ. Adipose tissue distribution, inflammation and its metabolic consequences, including Diabetes and Cardiovascular Disease. Front Cardiovasc Med. 2020;7:22. https://doi.org/10.3389/fcvm.2020.00022.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Hammarstedt A, Gogg S, Hedjazifar S, Nerstedt A, Smith U. Impaired adipogenesis and dysfunctional adipose tissue in human hypertrophic obesity. Physiol Rev. 2018;98(4):1911–41. https://doi.org/10.1152/physrev.00034.2017.

    Article  CAS  PubMed  Google Scholar 

  13. Xia W, Yu H, Huang Y, Yang Y, Shi L. The visceral adiposity index predicts the severity of hyperlipidaemic acute pancreatitis. Intern Emerg Med. 2022;17(2):417–22. https://doi.org/10.1007/s11739-021-02819-4.

    Article  PubMed  Google Scholar 

  14. Zhou Y, Hao N, Duan Z, Kong M, Xu M, Zhang D, Xu X, Yuan Q, Li C. Assessment of Acute Pancreatitis Severity and Prognosis with CT-Measured body composition. Int J Gen Med. 2021;14:3971–80. https://doi.org/10.2147/IJGM.S322589.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Yang X, He J, Ma S, Wang T, Zhu Q, Cao F, Li Y, Yang C, Chen C, Lu G, Hu L, Liu J, Chen W. The role of comorbid hypertriglyceridemia and abdominal obesity in the severity of acute pancreatitis: a retrospective study. Lipids Health Dis. 2021;20(1):171. https://doi.org/10.1186/s12944-021-01597-4.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Longo M, Zatterale F, Naderi J, Parrillo L, Formisano P, Raciti GA, Beguinot F, Miele C. Adipose tissue dysfunction as determinant of obesity-Associated Metabolic complications. Int J Mol Sci. 2019;20(9):2358. https://doi.org/10.3390/ijms20092358.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Leiria LO, Tseng YH. Lipidomics of brown and white adipose tissue: implications for energy metabolism. Biochim Biophys Acta Mol Cell Biol Lipids. 2020;1865(10):158788. https://doi.org/10.1016/j.bbalip.2020.158788.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Maximus PS, Al Achkar Z, Hamid PF, Hasnain SS, Peralta CA. Adipocytokines: are they the theory of everything? Cytokine. 2020;133:155144. https://doi.org/10.1016/j.cyto.2020.155144.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Bokarewa M, Nagaev I, Dahlberg L, Smith U, Tarkowski A. Resistin, an adipokine with potent proinflammatory properties. J Immunol. 2005;174(9):5789–95. https://doi.org/10.4049/jimmunol.174.9.5789.

    Article  CAS  PubMed  Google Scholar 

  20. Silswal N, Singh AK, Aruna B, Mukhopadhyay S, Ghosh S, Ehtesham NZ. Human resistin stimulates the pro-inflammatory cytokines TNF-alpha and IL-12 in macrophages by NF-kappaB-dependent pathway. Biochem Biophys Res Commun. 2005;334(4):1092–101. https://doi.org/10.1016/j.bbrc.2005.06.202.

    Article  CAS  PubMed  Google Scholar 

  21. Kawanami D, Maemura K, Takeda N, Harada T, Nojiri T, Imai Y, Manabe I, Utsunomiya K, Nagai R. Direct reciprocal effects of resistin and adiponectin on vascular endothelial cells: a new insight into adipocytokine-endothelial cell interactions. Biochem Biophys Res Commun. 2004;314(2):415–9. https://doi.org/10.1016/j.bbrc.2003.12.104.

    Article  CAS  PubMed  Google Scholar 

  22. Burnett MS, Lee CW, Kinnaird TD, Stabile E, Durrani S, Dullum MK, Devaney JM, Fishman C, Stamou S, Canos D, Zbinden S, Clavijo LC, Jang GJ, Andrews JA, Zhu J, Epstein SE. The potential role of resistin in atherogenesis. Atherosclerosis. 2005;182(2):241–8. https://doi.org/10.1016/j.atherosclerosis.2005.02.014.

    Article  CAS  PubMed  Google Scholar 

  23. Gruen ML, Hao M, Piston DW, Hasty AH. Leptin requires canonical migratory signaling pathways for induction of monocyte and macrophage chemotaxis. Am J Physiol Cell Physiol. 2007;293(5):C1481–8. https://doi.org/10.1152/ajpcell.00062.2007.

    Article  CAS  PubMed  Google Scholar 

  24. Kim SY, Lim JH, Choi SW, Kim M, Kim ST, Kim MS, Cho YS, Chun E, Lee KY. Preferential effects of leptin on CD4 T cells in central and peripheral immune system are critically linked to the expression of leptin receptor. Biochem Biophys Res Commun. 2010;394(3):562–8. https://doi.org/10.1016/j.bbrc.2010.03.019.

    Article  CAS  PubMed  Google Scholar 

  25. Saucillo DC, Gerriets VA, Sheng J, Rathmell JC, Maciver NJ. Leptin metabolically licenses T cells for activation to link nutrition and immunity. J Immunol. 2014;192(1):136–44. https://doi.org/10.4049/jimmunol.1301158.

    Article  CAS  PubMed  Google Scholar 

  26. Gerriets VA, Danzaki K, Kishton RJ, Eisner W, Nichols AG, Saucillo DC, Shinohara ML, MacIver NJ. Leptin directly promotes T-cell glycolytic metabolism to drive effector T-cell differentiation in a mouse model of autoimmunity. Eur J Immunol. 2016;46(8):1970–83. https://doi.org/10.1002/eji.201545861.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Luo Y, Liu M. Adiponectin: a versatile player of innate immunity. J Mol Cell Biol. 2016;8(2):120–8. https://doi.org/10.1093/jmcb/mjw012.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Choi HM, Doss HM, Kim KS. Multifaceted physiological roles of adiponectin in inflammation and diseases. Int J Mol Sci. 2020;21(4):1219. https://doi.org/10.3390/ijms21041219.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Xuan D, Han Q, Tu Q, Zhang L, Yu L, Murry D, Tu T, Tang Y, Lian JB, Stein GS, Valverde P, Zhang J, Chen J. Epigenetic modulation in Periodontitis: Interaction of Adiponectin and JMJD3-IRF4 Axis in macrophages. J Cell Physiol. 2016;231(5):1090–6. https://doi.org/10.1002/jcp.25201.

    Article  CAS  PubMed  Google Scholar 

  30. Ryu J, Hadley JT, Li Z, Dong F, Xu H, Xin X, Zhang Y, Chen C, Li S, Guo X, Zhao JL, Leach RJ, Abdul-Ghani MA, DeFronzo RA, Kamat A, Liu F, Dong LQ. Adiponectin alleviates Diet-Induced inflammation in the liver by suppressing MCP-1 expression and macrophage infiltration. Diabete. 2021;70(6):1303–16. https://doi.org/10.2337/db20-1073.

    Article  CAS  Google Scholar 

  31. Araki H, Nishihara T, Matsuda M, Fukuhara A, Kihara S, Funahashi T, Kataoka TR, Kamada Y, Kiyohara T, Tamura S, Hayashi N, Shimomura I. Adiponectin plays a protective role in caerulein-induced acute pancreatitis in mice fed a high-fat diet. Gut. 2008;57(10):1431–40. https://doi.org/10.1136/gut.2007.135665.

    Article  CAS  PubMed  Google Scholar 

  32. Dikmen K, Bostanci H, Gobut H, Yavuz A, Alper M, Kerem M. Recombinant adiponectin inhibits inflammation processes via NF-kB pathway in acute pancreatitis. Bratisl Lek Listy. 2018;119(10):619–24. https://doi.org/10.4149/BLL_2018_110.

    Article  CAS  PubMed  Google Scholar 

  33. He S, Zhang H, Lu Y, Zhang Z, Zhang X, Zhou N, Hu Z. Nampt promotes osteogenic differentiation and lipopolysaccharide-induced interleukin-6 secretion in osteoblastic MC3T3-E1 cells. Aging. 2021;13(4):5150–63. https://doi.org/10.18632/aging.202434.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Romacho T, Valencia I, Ramos-González M, Vallejo S, López-Esteban M, Lorenzo O, Cannata P, Romero A, San Hipólito-Luengo A, Gómez-Cerezo JF, Peiró C, Sánchez-Ferrer CF. Visfatin/eNampt induces endothelial dysfunction in vivo: a role for toll-like receptor 4 and NLRP3 inflammasome. Sci Rep. 2020;10(1):5386. https://doi.org/10.1038/s41598-020-62190-w.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Cui H, Du X, Liu C, Chen S, Cui H, Liu H, Wang J, Zheng Z. Visfatin promotes intervertebral disc degeneration by inducing IL-6 expression through the ERK/JNK/p38 signalling pathways. Adipocyte. 2021;10(1):201–15. https://doi.org/10.1080/21623945.2021.1910155.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Yang J, Liu M, Wang S, Gan Y, Chen X, Tao Y, Gao J. Alteration of Peripheral Resistin and the severity of Acute Pancreatitis: a Meta-analysis. Front Med (Lausanne). 2022;9:915152. https://doi.org/10.3389/fmed.2022.915152.

    Article  PubMed  Google Scholar 

  37. Li Y, Yang Q, Cai D, Guo H, Fang J, Cui H, Gou L, Deng J, Wang Z, Zuo Z. Resistin, a novel host defense peptide of Innate Immunity. Front Immunol. 2021;12:699807. https://doi.org/10.3389/fimmu.2021.699807.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Shi Y, Zhu N, Qiu Y, Tan J, Wang F, Qin L, Dai A. Resistin-like molecules: a marker, mediator and therapeutic target for multiple diseases. Cell Commun Signal. 2023;21(1):18. https://doi.org/10.1186/s12964-022-01032-w.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Filková M, Haluzík M, Gay S, Senolt L. The role of resistin as a regulator of inflammation: implications for various human pathologies. Clin Immunol. 2009;133(2):157–70. https://doi.org/10.1016/j.clim.2009.07.013.

    Article  CAS  PubMed  Google Scholar 

  40. Ghosh S, Singh AK, Aruna B, Mukhopadhyay S, Ehtesham NZ. The genomic organization of mouse resistin reveals major differences from the human resistin: functional implications. Gene. 2003;305(1):27–34. https://doi.org/10.1016/s0378-1119(02)01213-1.

    Article  CAS  PubMed  Google Scholar 

  41. Pang SS, Le YY. Role of resistin in inflammation and inflammation-related diseases. Cell Mol Immunol. 2006;3(1):29–34. http://www.cmi.ustc.edu.cn/3/1/29.pdf.

    CAS  PubMed  Google Scholar 

  42. Mostafazadeh M, Haiaty S, Rastqar A, Keshvari M. Correlation between Resistin Level and metabolic syndrome component: a review. Horm Metab Res. 2018;50(7):521–36. https://doi.org/10.1055/a-0637-1975.

    Article  CAS  PubMed  Google Scholar 

  43. Jang JC, Chen G, Wang SH, Barnes MA, Chung JI, Camberis M, Le Gros G, Cooper PJ, Steel C, Nutman TB, Lazar MA, Nair MG. Macrophage-derived human resistin is induced in multiple helminth infections and promotes inflammatory monocytes and increased parasite burden. PLoS Pathog. 2015;11(1):e1004579. https://doi.org/10.1371/journal.ppat.1004579.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Mantula PS, Outinen TK, Jaatinen P, Hämäläinen M, Huhtala H, Pörsti IH, Vaheri A, Mustonen JT, Mäkelä SM. High plasma resistin associates with severe acute kidney injury in Puumala hantavirus infection. PLoS ONE. 2018;13(12):e0208017. https://doi.org/10.1371/journal.pone.0208017.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Jiang S, Park DW, Tadie JM, Gregoire M, Deshane J, Pittet JF, Abraham E, Zmijewski JW. Human resistin promotes neutrophil proinflammatory activation and neutrophil extracellular trap formation and increases severity of acute lung injury. J Immunol. 2014;192(10):4795–803. https://doi.org/10.4049/jimmunol.1302764.

    Article  CAS  PubMed  Google Scholar 

  46. Jang JC, Li J, Gambini L, Batugedara HM, Sati S, Lazar MA, Fan L, Pellecchia M, Nair MG. Human resistin protects against endotoxic shock by blocking LPS-TLR4 interaction. Proc Natl Acad Sci U S A. 2017;114(48):E10399–408. https://doi.org/10.1073/pnas.1716015114.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Chang ML, Liang KH, Ku CL, Lo CC, Cheng YT, Hsu CM, Yeh CT, Chiu CT. Resistin reinforces interferon λ-3 to eliminate hepatitis C virus with fine-tuning from RETN single-nucleotide polymorphisms. Sci Rep. 2016;6:30799. https://doi.org/10.1038/srep30799.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Kwak MS, Lim JW, Kim H. Astaxanthin inhibits Interleukin-6 expression in Cerulein/Resistin-Stimulated pancreatic Acinar cells. Mediators Inflamm. 2021;2021:5587297. https://doi.org/10.1155/2021/5587297.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Jiang CY, Wang W, Tang JX, Yuan ZR. The adipocytokine resistin stimulates the production of proinflammatory cytokines TNF-α and IL-6 in pancreatic acinar cells via NF-κB activation. J Endocrinol Invest. 2013;36(11):986–92. https://doi.org/10.3275/9002. Epub 2013 Jun 10.

    Article  CAS  PubMed  Google Scholar 

  50. Jiang CY, Wang W. Resistin aggravates the expression of proinflammatory cytokines in cerulein stimulated AR42J pancreatic acinar cells. Mol Med Rep. 2017;15(1):502–6. https://doi.org/10.3892/mmr.2016.6027.

    Article  CAS  PubMed  Google Scholar 

  51. Wang WY, Chen Y, Su X, Tang D, Ben QW, Yao WY, Chen P, Yuan YZ. Resistin-Like Molecule-α causes Lung Injury in rats with Acute Pancreatitis by activating the PI-3K/Akt-NF-κB pathway and promoting inflammatory cytokine release. Curr Mol Med. 2016;16(7):677–87. https://doi.org/10.2174/1566524016666160802145700.

    Article  CAS  PubMed  Google Scholar 

  52. Lee Y, Lim JW, Kim H. α–lipoic acid inhibits cerulein/resistin–induced expression of interleukin–6 by activating peroxisome proliferator–activated receptor–γ in pancreatic acinar cells. Mol Med Rep. 2022;26(2):264. https://doi.org/10.3892/mmr.2022.12780.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Kiernan K, MacIver NJ. The role of the Adipokine Leptin in Immune cell function in Health and Disease. Front Immunol. 2021;11:622468. https://doi.org/10.3389/fimmu.2020.622468.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. de Candia P, Prattichizzo F, Garavelli S, Alviggi C, La Cava A, Matarese G. The pleiotropic roles of leptin in metabolism, immunity, and cancer. J Exp Med. 2021;218(5):e20191593. https://doi.org/10.1084/jem.20191593.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Tsiotra PC, Boutati E, Dimitriadis G, Raptis SA. High insulin and leptin increase resistin and inflammatory cytokine production from human mononuclear cells. Biomed Res Int. 2013;2013:487081. https://doi.org/10.1155/2013/487081.

    Article  CAS  PubMed  Google Scholar 

  56. Mancuso P, Curtis JL, Freeman CM, Peters-Golden M, Weinberg JB, Myers MG Jr. Ablation of the leptin receptor in myeloid cells impairs pulmonary clearance of Streptococcus pneumoniae and alveolar macrophage bactericidal function. Am J Physiol Lung Cell Mol Physiol. 2018;315(1):L78–L86. https://doi.org/10.1152/ajplung.00447.2017.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Xu X, Huang X, Zhang L, Huang X, Qin Z, Hua F. Adiponectin protects obesity-related glomerulopathy by inhibiting ROS/NF-κB/NLRP3 inflammation pathway. BMC Nephrol. 2021;22(1):218. https://doi.org/10.1186/s12882-021-02391-1.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Wolf AM, Wolf D, Rumpold H, Enrich B, Tilg H. Adiponectin induces the anti-inflammatory cytokines IL-10 and IL-1RA in human leukocytes. Biochem Biophys Res Commun. 2004;323(2):630–5. https://doi.org/10.1016/j.bbrc.2004.08.145.

    Article  CAS  PubMed  Google Scholar 

  59. Kuang ZS, Leng YX, Yang N, Li ZQ, Zong YN, Han DY, Li Y, He JD, Mi XN, Cong ZK, Zhu X, Wu CY, Guo XY. Inhibition of visfatin alleviates sepsis-induced intestinal damage by inhibiting Hippo signaling pathway. INFLAMM RES. 2022;71(7–8):911–22. https://doi.org/10.1007/s00011-022-01593-z.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Mercurio L, Morelli M, Scarponi C, Scaglione GL, Pallotta S, Avitabile D, Albanesi C, Madonna S. Enhanced NAMPT-Mediated NAD Salvage Pathway Contributes to Psoriasis Pathogenesis by amplifying epithelial auto-inflammatory circuits. Int J Mol Sci. 2021;22(13):6860. https://doi.org/10.3390/ijms22136860.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Philp AM, Butterworth S, Davis ET, Jones SW. eNAMPT is localised to areas of cartilage damage in patients with hip osteoarthritis and promotes cartilage catabolism and inflammation. Int J Mol Sci. 2021;22(13):6719. https://doi.org/10.3390/ijms22136719.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Yao S, Jiang C, Zhang H, Gao X, Guo Y, Cao Z. Visfatin regulates pg LPS-induced proinflammatory/prodegradative effects in healthy and inflammatory periodontal cells partially via NF-κB pathway. Biochim Biophys Acta Mol Cell Res. 2021;1868(8):119042. https://doi.org/10.1016/j.bbamcr.2021.119042.

    Article  CAS  PubMed  Google Scholar 

  63. Zhang Y, Zhang Y, Zhuang R, Ma Y, Zhang C, Tang K, Yi H, Jin B. Adiponectin’s globular domain inhibits T cell activation by interacting with LAIR-1. Biochem Biophys Res Commun. 2021;573:117–24. https://doi.org/10.1016/j.bbrc.2021.08.025.

    Article  CAS  PubMed  Google Scholar 

  64. Sayers SR, Beavil RL, Fine NHF, Huang GC, Choudhary P, Pacholarz KJ, Barran PE, Butterworth S, Mills CE, Cruickshank JK, Silvestre MP, Poppitt SD, McGill AT, Lavery GG, Hodson DJ, Caton PW. Structure-functional changes in eNAMPT at high concentrations mediate mouse and human beta cell dysfunction in type 2 diabetes. Diabetologia. 2020;63(2):313–23. https://doi.org/10.1007/s00125-019-05029-y.

    Article  CAS  PubMed  Google Scholar 

  65. Biberci Keskin E, Büyükaydın B, Soysal P, Kiremitçi S, Yabacı A, Şentürk H. The impact of obesity on acute pancreatitis outcomes in older patients. Eur Geriatr Med. 2020;11(3):427–32. https://doi.org/10.1007/s41999-020-00305-2.

    Article  PubMed  Google Scholar 

  66. Khatua B, El-Kurdi B, Patel K, Rood C, Noel P, Crowell M, Yaron JR, Kostenko S, Guerra A, Faigel DO, Lowe M, Singh VP. Adipose saturation reduces lipotoxic systemic inflammation and explains the obesity paradox. Sci Adv. 2021;7(5):eabd6449. https://doi.org/10.1126/sciadv.abd6449.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Kisaoglu A, Aydinli B, Ozturk G, Atamanalp S, Ozogul B, Yildirgan M, Polat K. Severity markers in patients with acute pancreatitis. Open Med (Wars). 2014;9(4):556–64. https://doi.org/10.2478/s11536-014-0501-5.

    Article  CAS  Google Scholar 

  68. Schäffler A, Hamer O, Dickopf J, Goetz A, Landfried K, Voelk M, Herfarth H, Kopp A, Büchler C, Schölmerich J, Brünnler T. Admission resistin levels predict peripancreatic necrosis and clinical severity in acute pancreatitis. Am J Gastroenterol. 2010;105(11):2474–84. https://doi.org/10.1038/ajg.2010.278.

    Article  CAS  PubMed  Google Scholar 

  69. Kibar YI, Albayrak F, Arabul M, Dursun H, Albayrak Y, Ozturk Y. Resistin: New serum marker for predicting severity of acute pancreatitis. J Int Med Res. 2016;44(2):328–37. https://doi.org/10.1177/0300060515605428.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Singh AK, Dawra S, Rana S, Gupta P, Samanta J, Sinha SK, Gupta V, Yadav TD, Kochhar R. Can serum resistin predict severity of acute pancreatitis? Biomarkers. 2021;26(1):31–7. https://doi.org/10.1080/1354750X.2020.1841295.

    Article  CAS  PubMed  Google Scholar 

  71. Karpavicius A, Dambrauskas Z, Gradauskas A, Samuilis A, Zviniene K, Kupcinskas J, Brimas G, Meckovski A, Sileikis A, Strupas K. The clinical value of adipokines in predicting the severity and outcome of acute pancreatitis. BMC Gastroenterol. 2016;16(1):99. https://doi.org/10.1186/s12876-016-0514-4.

    Article  PubMed  PubMed Central  Google Scholar 

  72. Al-Maramhy H, Abdelrahman AI, Sawalhi S. Resistin is not an appropriate biochemical marker to predict severity of acute pancreatitis: a case-controlled study. World J Gastroenterol. 2014;20(41):15351–7. https://doi.org/10.3748/wjg.v20.i41.15351.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Yu P, Wang S, Qiu Z, Bai B, Zhao Z, Hao Y, Wang Q, Guo M, Feng X, Zhu J, Feng Q, Zhao Q. Efficacy of resistin and leptin in predicting persistent organ failure in patients with acute pancreatitis. Pancreatology. 2016;16(6):952–7. https://doi.org/10.1016/j.pan.2016.09.002.

    Article  CAS  PubMed  Google Scholar 

  74. Muddana V, Evans AC, Langmead CJ, Clermont G, Barmada MM, Papachristou GI, Whitcomb DC. Resistin, a potent adipokine, is associated with acute pancreatitis: Assessment of functional genetic polymorphisms and serum levels. Gastroenterology. 2010;138(5):66.

    Article  Google Scholar 

  75. Novotný D, Malina P, Krumpholcova P, Tozzi I, Prochazka V. The acute pancreatitis severity prediction using adiponectin, adipocyte fatty acid binding protein and fibroblast growth factor 21 levels in day 4 after admission. Klinicka Biochemie a Metabolismus. 2015;23(1):9–16.

    Google Scholar 

  76. Sharma A, Muddana V, Lamb J, Greer J, Papachristou GI, Whitcomb DC. Low serum adiponectin levels are associated with systemic organ failure in acute pancreatitis. Pancreas. 2009;38(8):907–12. https://doi.org/10.1097/MPA.0b013e3181b65bbe.

    Article  CAS  PubMed  Google Scholar 

  77. Tukiainen E, Kylanpaa ML, Ebeling P, Kemppainen E, Puolakkainen P, Repo H. Leptin and adiponectin levels in acute pancreatitis. Pancreas. 2006;32(2):211–4. https://doi.org/10.1097/01.mpa.0000202940.47837.89.

    Article  CAS  PubMed  Google Scholar 

  78. Türkoğlu A, Böyük A, Tanrıverdi MH, Gündüz E, Dusak A, Kaplan İ, Gümüş M. The potential role of BMI, plasma leptin, nesfatin-1 and ghrelin levels in the early detection of pancreatic necrosis and severe acute pancreatitis: a prospective cohort study. Int J Surg. 2014;12(12):1310–3. https://doi.org/10.1016/j.ijsu.2014.10.040.

    Article  PubMed  Google Scholar 

  79. Panek J, Bonior J, Pieton J, Jaworek J. Serum leptin and ghrelin levels in patients in the early stages of acute biliary pancreatitis and different degrees of severity. Pol Przegl Chir. 2014;86(5):211–7. https://doi.org/10.2478/pjs-2014-0038.

    Article  PubMed  Google Scholar 

  80. Duarte-Rojo A, Lezama-Barreda A, Ramirez-Iglesias MT, Peláez-Luna M, Robles-Díaz G. Is leptin related to systemic inflammatory response in acute pancreatitis? World J Gastroenterol. 2006;12(27):4392–6. https://doi.org/10.3748/wjg.v12.i27.4392.

    Article  PubMed  PubMed Central  Google Scholar 

  81. Schäffler A, Hamer OW, Dickopf J, Goetz A, Landfried K, Voelk M, Herfarth H, Kopp A, Buechler C, Schölmerich J, Brünnler T. Admission visfatin levels predict pancreatic and peripancreatic necrosis in acute pancreatitis and correlate with clinical severity. Am J Gastroenterol. 2011;106(5):957–67. https://doi.org/10.1038/ajg.2010.503.

    Article  CAS  PubMed  Google Scholar 

  82. Ülger BV, Gül M, Uslukaya Ö, Oğuz A, Bozdağ Z, Yüksel H, Böyük A. New hormones to predict the severity of gallstone-induced acute pancreatitis. Turk J Gastroenterol. 2014;25(6):714–7. https://doi.org/10.5152/tjg.2014.6201.

    Article  PubMed  Google Scholar 

  83. Deng LH, Hu C, Cai WH, Chen WW, Zhang XX, Shi N, Huang W, Ma Y, Jin T, Lin ZQ, Jiang K, Guo J, Yang XN, Xia Q. Plasma cytokines can help to identify the development of severe acute pancreatitis on admission. Med (Baltim). 2017;96(28):e7312. https://doi.org/10.1097/MD.0000000000007312.

    Article  CAS  Google Scholar 

  84. Langmead C, Lee PJ, Paragomi P, Greer P, Stello K, Hart PA, Whitcomb DC, Papachristou GI. A novel 5-Cytokine panel outperforms conventional predictive markers of persistent organ failure in Acute Pancreatitis. Clin Transl Gastroenterol. 2021;12(5):e00351. https://doi.org/10.14309/ctg.0000000000000351.

    Article  PubMed  PubMed Central  Google Scholar 

  85. Malina P, Novotny D, Krumpholcova P, Tozzi I, Prochazka V, Malina P. Possibility of prediction of acute pancreatitis severity by determination of adipokines (adiponectin, FGF-21 and A-FABP) during hospitalization. Klinicka Biochemie a Metabolismus. 2014;22(1):16–21.

    Google Scholar 

  86. Guo F, Dong X, Ma X, Li C, Dong C. Correlation between thyroid function and serum visfatin in patients with acute pancreatitis. Chin J Endemiology. 2021;41(8):660–3. https://doi.org/10.3760/cma.j.cn231583-20191111-00315. (2021).

    Article  Google Scholar 

Download references

Acknowledgements

We appreciate from both Department of Gastroenterology, Hebei Provincial People’s Hospital and Graduate School of Hebei North University.

Funding

Special Project for the Construction of Academician Workstation of Hebei Provincial People’s Hospital (Project No. 199A7745H). Clinical significance and mechanism of leukocyte elevation in the third condition of severe acute pancreatitis (Project No. 20200747). Molecular mechanism of NNMT/CCL8/VEGF-C signaling axis regulating lymph node metastasis in gastric cancer (Project No. H2022307040).

Author information

Authors and Affiliations

  1. Hebei North University, Zhangjiakou, 075132, China

    Xuehua Yu

  2. Department of Gastroenterology, Hebei General Hospital, No.348, Heping West Road, Shijiazhuang, Hebei Province, 050057, China

    Xuehua Yu, Ning Zhang, Jing Wu, Yunhong Zhao & Gaifang Liu

  3. Hebei Medical University, Shijiazhuang, 050011, China

    Ning Zhang

  4. Department of Gastroenterology, Anhui Medical University, He Fei, 230601, China

    Chengjiang Liu

Authors
  1. Xuehua Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ning Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jing Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yunhong Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Chengjiang Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Gaifang Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Jing Wu, Xuehua Yu, and Ning Zhang participated in literature collection.Yunhong Zhao, Xuehua Yu, and Ning Zhang participated in data extraction.Chengjiang Liu, Xuehua Yu, and Ning Zhang involved in article quality assessment.Xuehua Yu wrote the manuscript, and Gaifang Liu revised the article critically for important intellectual content.

Corresponding author

Correspondence to Gaifang Liu.

Ethics declarations

Ethics approval and consent to participate

Not applicable (this paper was provided based on researching in global databases).

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1:

Forest plots of subgroup analysis by year of publication (A), age (B), sample size (C), and definition of SAP group and MAP group (D) in resistin

Supplementary Material 2:

Forest plots of subgroup analysis by year of publication (A), age (B), sample size (C), and definition of SAP group and MAP group (D) in leptin

Supplementary Material 3:

Forest plots of subgroup analysis by year of publication (A), age (B), sample size (C), and definition of SAP group and MAP group (D) in adiponectin

Supplementary Material 4:

Forest plots of SMD with 95% CI of peripheral blood levels of resistin excluding the studies of Kibar YI et al., Singh AK et al. and Langmead C et al

Supplementary Material 5:

Forest plots of SMD with 95% CI of peripheral blood levels of leptin excluding the studies of Türkoğlu A et al

Supplementary Material 6:

Begg’s funnel plot of peripheral blood levels of resistin (A), leptin (B), and adiponectin (C) levels between SAP patients and MAP patients

Supplementary Material 7:

Egger’s publication bias plot of peripheral blood levels of resistin (A), leptin (B), and adiponectin (C) levels between SAP patients and MAP patients

Supplementary Material 8:

Search strategy in PubMed

Supplementary Material 9:

Characteristics of 20 studies included in the meta-analysis (2)

Supplementary Material 10:

Characteristics of 20 studies included in the meta-analysis (3)

Supplementary Material 11:

Characteristics of 20 studies included in the meta-analysis (4)

Supplementary Material 12:

The quality assessment of all included studies applying Quality Assessment of Diagnostic Accuracy Studies tool (QUADAS)

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yu, X., Zhang, N., Wu, J. et al. Predictive value of adipokines for the severity of acute pancreatitis: a meta-analysis. BMC Gastroenterol 24, 32 (2024). https://doi.org/10.1186/s12876-024-03126-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12876-024-03126-w

Keywords

BMC Gastroenterology

ISSN: 1471-230X

Contact us