Abstract
BACKGROUND:
Spontaneous bacterial peritonitis (SBP) represents a critical and potentially lethal condition that typically develops in individuals with liver cirrhosis. This meta-analysis aimed to assess diabetes mellitus (DM) as a risk factor for SBP in liver cirrhotic patients.
METHODS:
Following PRISMA guidelines, fifteen studies were included, for a total of 76 815 patients. The risk of bias was assessed using the Newcastle-Ottawa scale (NOS). We represented the results as risk ratios (RR) with the corresponding 95% confidence intervals (CI) using RevMan software. Additionally, we pooled the hazard ratios (HR) for developing SBP in patients with DM from the included studies.
RESULTS:
The meta-analysis shows a significantly increased risk of SBP in cirrhotic patients with DM (HR: 1.26; 95% CI [1.05-1.51], P=.01; HR: 1.70; 95% CI [1.32-2.18], P<.001).
CONCLUSIONS:
The study signifies that DM is an independent risk factor for SBP, emphasizing the need for targeted preventive measures in this specific population.
INTRODUCTION
Spontaneous bacterial peritonitis (SBP) represents a critical and potentially lethal condition that typically develops in individuals with liver cirrhosis.1,2 As the worldwide prevalence of liver cirrhosis increases,3 there is a growing imperative to investigate its associated complications. SBP is a highly serious condition that can greatly increase both morbidity and mortality rates in people with cirrhosis.1,4,5 This peritoneal cavity infection, while frequently manageable, can quickly proceed to severe sepsis or septic shock, particularly in individuals with advanced liver disease.6 The early detection and management of SBP are crucial yet remain challenging due to its often-subtle clinical presentation.6–8 Understanding the risk factors and patient populations at heightened risk for SBP is essential for improving outcomes.
The coexistence of liver cirrhosis and diabetes mellitus (DM) gives rise to a multifaceted clinical situation.9 DM is a persistent hyperglycemia-associated chronic metabolic disorder that is linked to a diverse array of complications that affect various organ systems.10 The global incidence of this condition is on the rise, affecting an estimated 530 million adults,11 rendering it a substantial public health issue. When considering liver cirrhosis, DM not only hastens the disease's progression but also increases the risk of contracting multiple infections, including SBP.9,12 Moreover, a retrospective study revealed that DM increases complications and reduces overall survival in cirrhotic patients.13 A population-based study indicates a 2.52 times higher risk of death at five years among individuals with type 2 DM when compared to the general populace.14 However, the comparison of complication rates in cirrhotic patients with type 2 DM is challenging due to the high heterogeneity and limited focus in existing studies.
Changes in the immune system can be caused by DM, which can impair phagocytosis and the function of neutrophils, which are essential for fighting infections.15–17 Moreover, in diabetic patients, the hyperglycemic environment may promote bacterial proliferation, thereby augmenting the susceptibility to infections such as SBP. 18,19 The coexistence of DM and liver cirrhosis may increase the risk of severe complications in a synergistic manner,20–22 which can substantially complicate the clinical management of these patients. Liu et al's meta-analysis points to the correlation between type 2 diabetes mellitus (DM) and elevated mortality and hepatocellular carcinoma rates among cirrhotic patients, with inflammation and an increased incidence of liver malignancy being identified as contributing factors.23 However, challenges in comparing complication rates arise from high heterogeneity and limited studies focusing on cirrhotic diabetic patients.
Liver transplantation represents the definitive therapeutic intervention for liver cirrhosis, offering the only curative option currently available,24 so prevention is key,25,26 as the progression to severe complications such as SBP often marks a turning point in the disease trajectory, frequently associated with increased hospitalizations, escalated healthcare costs, and a substantial decline in quality of life.27 The study aimed to investigate whether diabetes mellitus (DM) acts as a distinct risk factor for the development of spontaneous bacterial peritonitis (SBP) in individuals with liver cirrhosis, irrespective of other potential factors. The study results may inform and enhance preventive strategies for SBP.
METHODS
This systematic review and meta-analysis were conducted strictly in accordance with the PRISMA statement guidelines.28 All procedures adhered to the methodologies outlined in the Cochrane Handbook of Systematic Reviews and Meta-analysis of Interventions. Prior to commencement, the protocol was predefined and registered on the International Prospective Register of Systematic Reviews (PROSPERO) with the designated ID (CRD42024497609).
Study designs could be controlled studies, either clinical trials or prospective and retrospective observational studies. We considered studies that compared diabetic patients versus non-diabetic patients regarding the incidence of SBP and also studies comparing patients with SBP versus patients without SBP regarding the presence of DM at the baseline.
We excluded studies with unreliable data for extraction and analysis, those reported solely as abstracts or theses, case reports, review articles, animal studies, and studies lacking complete full-text availability. We did not include studies that were not written in English.
We conducted an extensive search of four electronic databases (PubMed, Scopus, Web of Science, and Cochrane Central Register of Controlled Trials) until 1 January 2024 without date limit using the following query: (Diabetes Mellitus OR Diabetes OR DM) AND (Spontaneous Bacterial Peritonitis OR Primary Bacterial Peritonitis OR Bacterial Peritonitis)”. Further, the references of the included studies were manually searched for any potentially eligible studies. Supplementary Table 1 shows the detailed search strategy and results for each database.
Selection process
Duplicates were eliminated utilizing Endnote (Clarivate Analytics, PA, USA), and the retrieved references underwent a two-step screening process. The selection process was done by using the Rayyan website.29 Initially, titles and abstracts of all identified articles were independently reviewed by two authors to evaluate their relevance to this meta-analysis. Subsequently, full-text articles corresponding to the identified abstracts were screened by two independent reviewers to determine final eligibility for inclusion in the meta-analysis. Conflicts were resolved in case of disagreement through a third reviewer consulted to achieve consensus.
Data collection process and data items
Data were extracted into a uniform data extraction sheet. The extracted data included (1) a summary of the included studies (study ID, study design, country, sample size, population, exposure/indicator, comparison, and main finding), (2) characteristics of the population of included studies (age, sex, etiology of cirrhosis, history of variceal bleeding or hepatic encephalopathy, Child score, Model for End-Stage Liver Disease (MELD) score, serum bilirubin prior to admission, ascites protein, and proton pump inhibitor usage), (3) quality assessment domains, and (4) outcome measures (the incidence of SBP) (Figure 1).
Figure 1.
The PRISMA flow diagram showing the study selection process.
Assessing the risk of bias in individual studies and across studies
We used the Newcastle Ottawa scale (NOS) to assess the quality of the included studies.30 This tool assesses the quality of observational studies by evaluating three crucial domains: the selection of study subjects, comparability of groups in terms of demographic characteristics and key potential confounders, and the ascertainment of the prespecified outcome. Additionally, to investigate publication bias across studies, funnel plots were constructed to visualize the relationship between effect size and standard error. The following two methods were used to assess the evidence of publication bias across studies: Failsafe N,31 Begg and Mazumdar rank correlation test (Kendall's tau,32 and regression test for funnel plot asymmetry using Jamovi software (version 1.6 for Windows).
Synthesis methods
We opted to present the results as risk ratios (RR) comparing the incidence of spontaneous bacterial peritonitis (SBP) in patients with diabetes mellitus (DM) versus those without DM, as all study outcomes were dichotomous data. To synthesize the RR and their corresponding 95% confidence intervals (CI), we employed the DerSimonian-Liard meta-analysis model. Additionally, we gathered pooled hazard ratios (HRs) for developing SBP in patients with DM from the included studies, utilizing the inverse variance method and applying the DerSimonian-Liard meta-analysis model. The meta-analysis was conducted using Review Manager software (version 5.4 for Windows) and Jamovi software (version 2.3.18 for Windows).
Statistical heterogeneity among studies was assessed using the Chi-square test (Cochrane Q test). The chi-square statistic, Cochrane Q, was then used to compute the I-squared value according to the equation: I2=((Q − df)/Q) × 100%. A chi-square P value less than 0.1 was considered indicative of significant heterogeneity. I-squared values ?50% were indicative of high heterogeneity.
To evaluate the robustness of the evidence, we conducted a certainty assessment through sensitivity analysis, also known as leave-one-out meta-analysis. For each outcome in the meta-analysis, we performed sensitivity analysis in multiple scenarios, excluding one study in each scenario, to ensure that the overall effect size was not dependent on any single study.
RESULTS
Our literature search initially yielded 416 records. Following title and abstract screening, 16 articles were deemed eligible for full-text screening (Table 1). Among these, only six studies met the inclusion criteria for the meta-analysis. Meanwhile, through a manual search of references from the included studies and additional exploration via Google Scholar, 14 more relevant studies were identified. Of these, nine were included in the meta-analysis. In total, 15 studies were included in our analysis. The PRISMA flow diagram illustrating the study selection process is provided in Figure 1.
Table 1.
Summary of the studies included in this systematic review and meta-analysis.
| Study ID | Country | Study design | Sample size | Population | Exposure/Indicator | Comparison | Main outcome finding |
|---|---|---|---|---|---|---|---|
| Bajaj 200935 | USA | Case-control study | 140 | Hospitalized patients have known cases of cirrhosis with ascites. | Presence of SBP | No SBP | No difference between the two groups regarding the presence of DM at baseline. |
| Bossen 201936 | Denmark | Prospective cohort study | 1,198 | Outpatients with cirrhosis and ascites. | DM | Non-DM | DM did not increase infection risk or mortality after infection |
| Campbell 200737 | USA | Case-control study | 116 | Cirrhotic patients with ascites who underwent diagnostic paracentesis. | Presence of SBP | No SBP | No difference between the two groups regarding the presence of DM at baseline. |
| Kraja 201238 | Albania | Cross-sectional study | 256 | Adults hospitalized cirrhotic patients with ascites. | Presence of SBP | No SBP | No difference between the two groups regarding the presence of DM at baseline. |
| Liu 201633 | USA | Prospective cohort study | 72731 | Adult patients are diagnosed with cirrhosis. | DM | Non-DM | DM increased the risk of decompensation events, including SBP, HE, ARF, HCC, ascites, and variceal bleeding in patients with compensated cirrhosis. |
| Mohammad 201639 | Egypt | Cross-sectional study | 176 | Adults hospitalized adults' cirrhotic ascetic patients. | Presence of SBP | No SBP | No difference between the two groups regarding the presence of DM at baseline. |
| Sigal 200640 | USA | Cross-sectional study | 54 | Patients with cirrhosis secondary to HCV and have HE. | DM | Non-DM | DM increased the severity of HE but did not impact the incidence of SBP. |
| Tergast 201841 | Italy | Retrospective cohort study | 475 | Hospitalized patients with newly diagnosed ascites. | DM | Non-DM | DM increased the risk for SBP. Overall mortality was numerically higher among DM patients. |
| Thiele 201442 | Brazil | Cross-sectional analytical study | 45 | Adults hospitalized individuals with liver cirrhosis and ascites. | Presence of SBP | No SBP | No difference between the two groups regarding the presence of DM at baseline. |
| Tu 202043 | China | Retrospective cohort study | 343 | Ascites liver cirrhosis patients. | Presence of SBP | No SBP | DM was more frequent in the SBP group. DM in cirrhotic patients may serve as a marker for increased risk and severity of adverse outcomes associated with SBP. |
| Wang 201834 | China | Retrospective Cohort Study | 390 | Hospitalized patients with liver cirrhosis | Presence of SBP | No SBP | No difference between the two groups regarding the presence of DM at baseline. |
| Wlazlo 201444 | Netherlands | Retrospective cohort study | 226 | Adult patients diagnosed with cirrhosis. | DM | Non-DM | DM increased the risk of SBP but did not increase the mortality. |
| Xue 202245 | China | Retrospective cohort study | 268 | Patients with decompensated cirrhosis and ascites. | Presence of SBP | No SBP | DM increased the risk of SBP. |
| Zhang 202046 | China | Retrospective cohort study | 214 | 'Adults hospitalized cirrhotic and ascitic patients | DM | Non-DM | DM increased the risk of SBP. |
| Zuwala-Jagiello 201947 | Poland | Retrospective cohort study | 330 | Adults hospitalized with liver cirrhosis | DM | Non-DM | Cirrhotic patients with DM had a higher incidence of SBP compared to those without DM. |
Fifteen studies were included in the meta-analysis, with a total of 76 962 patients with liver cirrhosis. In all studies, liver cirrhosis patients were classified as having DM or no DM, and the studies evaluated the incidence of SBP, or liver cirrhosis patients were classified as having SBP or no SBP, and the studies evaluated the presence of DM at the baseline. A summary of the included studies is provided in Tables 2a and 2b, and the baseline characteristics of the populations are reported in Table 3. Overall, the quality of all the studies was good, according to the Newcastle Ottawa scale (Supplementary Tables 2-4).
Table 2a.
Baseline characteristics of the studies included in this systematic review and meta-analysis.
| Study ID | Groups | Sample size | Age (years) | Sex (male) n (%) | Etiology (alcohol) n (%) | Etiology (biliary) n (%) | Etiology (autoimmune) n (%) |
|---|---|---|---|---|---|---|---|
| Bajaj 200935 | SBP | 70 | 55 (13) | 42 (60) | 21 (30) | NR | NR |
| No SBP | 70 | 54 (13) | 37 (52.9) | 19 (27) | NR | NR | |
| Bossen 201936 | DM | 289 | Median (IQR): 60 (55 67) | 210 (73) | 153 (53) | Other: 136 (47) | |
| No DM | 909 | Median (IQR): 56 (49–63) | 624 (69) | 540 (59) | Other: 369 (41) | ||
| Campbell 200737 | SBP | 32 | 53.9 (10.1) | 23 (72) | 5 (16) | Biliary and autoimmune: 2 (6) | |
| No SBP | 84 | 54.9 (11) | 55 (65) | 17 (20) | Biliary and autoimmune: 4 (5) | ||
| Kraja 201238 | SBP | 64 | 54.2 (12.65) | 48 (75) | 36 (56.3) | Other: 28 (43.7) | |
| No SBP | 192 | 54.56 (12.82) | 151 (78.6) | 100 (52.1) | Other: 92 (47.9) | ||
| Liu 201633 | DM | 20,477 | Under 40: 676 (3.3%) 40-59: 10,492 (51.24%) ≥60: 9309 (45.46%) | 11,159 (54.5) | 2337 (11.41) | 1038 (5.07) | Other: 17102 (83.52) |
| No DM | 52,254 | Under 40: 5534 (10.59%) 40-59: 30.191 (57.78%) ≥60: 16,529 (31.36%) | 27906 (53.4) | 9412 (18.01) | 5852 (11.2) | Other: 36990 (70.79) | |
| Mohammad 201639 | SBP | 54 | 54.46 (10.25) | 110 (62.5) | Infected hepatitis: 38 (70.73) Unknown: 16 (29.63) | ||
| No SBP | 122 | 56.20 (8.39) | Infected hepatitis: 85 (69.67) Unknown: 37 (30.33) | ||||
| Sigal 200640 | DM | 19 | 53.1 (6.4) | 15 (79) | NR | NR | NR |
| No DM | 35 | 49.3 (11.3) | 71.4) | NR | NR | NR | |
| Tergast 201841 | DM | 118 | 58.06 (10.36) | 79 (67) | 55 (46.6) | Other: 63 (53.4) | |
| No DM | 357 | 54.44 (11.13) | 200 (56) | 201 (56.3) | Other: 156 (43.7) | ||
| Thiele 201442 | SBP | 15 | 49.7 (13) | 12 (80) | 12 (85.7) | NR | NR |
| No SBP | 30 | 55 (11.8) | 25 (83.3) | 19 (67.9) | NR | NR | |
| Tu 202043 | SBP | 98 | 54.01 (13.6) | 79 (80.61) | 16 (16.33) | NR | 5 (5.1) |
| No SBP | 98 | 53.56 (13.47) | 71 (72.45) | 19 (19.39) | NR | 12 (12.24) | |
| Wang 201834 | SBP | 195 | 50.2 (11.8) | 149 (76.41) | 35 (17.95) | 17 (8.72) | NR |
| No SBP | 195 | 51.7 (10.6) | 132 (67.69) | 40 (20.51) | 16 (8.21) | NR | |
| Wlazlo 201444 | DM | 78 | 55.9 (11.9) | 50 (64.1) | 34 (43.6) | NR | 2 (2.6) |
| No DM | 148 | 65.4 (10.9) | 96 (64.9) | 103 (69.6) | NR | 7 (4.7) | |
| Xue 202245 | SBP | 98 | 56.3 (11.1) | 63 (64.3) | 18 (18.4) | Other: 80 (81.6) | |
| No SBP | 170 | 54.1 (10.9) | 118 (69.4) | 31 (18.2) | Other: 139 (81.8) | ||
| Zhang 202046 | DM | 21 | Median (range): 74 (60-76) | 10 (47.6) | 2 (0.9) | NR | 23 (10.7) |
| No DM | 193 | Median (range): 62 (52-71) | 111 (57.5) | ||||
| Zuwala-Jagiello 201947 | DM | 149 | Median (IQR): 65 (57-70) | 82 (55.03) | 67 (45) | Viral: 82 (55) | |
| No DM | 181 | Median (IQR): 57 (49-69) | 97 (53.6) XNN SAUDI MED 2 | 74 (41) | Viral: 107 (59) | ||
Age and MELD score are mean (standard deviation) except where indicated otherwise. DM: Diabetes mellitus; SBP: Spontaneous bacterial peritonitis; NR: Not reported; IQR: Interquartile range; SD: Standard deviation;
Table 2b.
Baseline characteristics of the studies included in this systematic review and meta-analysis.
| Study ID | History of variceal bleeding or upper Gl bleeding n (%) | History of hepatic encephalopathy n (%) | Child score | MELD score | Serum total bilirubin prior to admission (mg/dl) mean (SD) | Ascites protein (g/dL) mean (SD) | Proton pump inhibitors, any dose n (%) |
|---|---|---|---|---|---|---|---|
| Bajaj 200935 | 31 (44) | 19 (27) | Median (range): 11 (8-15) | Median (range): 14 (10-30) | 3.5 (7.2) | 1.0 (0.3) | 48 (69) |
| 28 (40) | 21 (30) | Median (range): 11 (8-14) | Median (range): 13 (11-28) | 4.1 (6.3) | 1.6 (0.9) | 22 (31) | |
| Bossen 201936 | Excluded | Excluded | Median (IQR): 8 (7-9) | Median (IQR): 14 (11-18) | NR | NR | 150 (52) |
| Excluded | Excluded | Median (IQR): 8 (7-10) | Median (IQR): 15 (11-18) | NR | NR | 374 (41) | |
| Campbell 200737 | NR | NR | NR | Median (IQR): 23 (18-29) | Median (IQR): 4.1 (2.6-7) | NR | 13 (41) |
| NR | NR | NR | Median (IQR): 18 (13-22) | Median (IQR): 2.6 (1.3-5.6) | NR | 30 (36) | |
| Kraja 201238 | NR | NR | NR | 23.2 (7.71) | 6.34 (7.15) | NR | NR |
| NR | NR | NR | 19.49 (7.8) | 5.41 (7.24) | NR | NR | |
| Liu 201633 | 2048 (10) | 3143 (15.35) | NR | NR | NR | NR | NR |
| 4312 (8.25) | 6928 (13.26) | NR | NR | NR | NR | NR | |
| Mohammad 201639 | NR | 41 (75.9) | B: 4 (7.41%) C: 50 (92.59%) | NR | Direct: 2.55 (2.62) | 1.21 (0.52) | 11 (20.37) |
| NR | NR | B: 19 (15.57%) C: 103 (84.43%) | NR | Direct: 2.6 (2.93) | 1.98 (0.60) | 3 (2.46) | |
| Sigal 200640 | 1 (5.3) | 19 (100) Mild: 7 (36.8) Severe: 12 (63.2) | NR | 12.8 (3.4) | 1.7 (0.95) | NR | NR |
| 8 (22.9) | 35 (100) Mild: 26 (74.3) Severe: 9 (25.7) | NR | 16.9 (5.6) | 4.3 (2.9) | NR | NR | |
| Tergast 201841 | 16 (14) | NR | NR | 18.97 (7.93) | 5.32 (9.01) | 13 (9) | 100 (85) |
| 50 (14) | NR | NR | 19.61 (7.7) | 6.61 (8.71) | 12 (8) | 285 (80) | |
| Thiele 201 442 | 5 (41.7) | 5 (41.7) | C: 8 (72.7%) | 22.2 (7.6) | 6.7 (5.9) | 1.2 (0.5) | NR |
| 8 (34.8) | 9 (32.1) | C: 8 (50%) | 17.9 (6.7) | 4.8 (6) | 1.0 (0.8) | NR | |
| Tu 202043 | 19 (19.39) | 21 (21.43) | A: 2 (2.04%) B: 35 (35.71%) C: 61 (62.24%) |
18.20 (11.04) | NR | NR | NR |
| 0 (0) | 3 (3.06) | A: 2 (2.04%) B: 58 (59.18%) C: 38 (38.78%) |
9.92 (5.88) | NR | NR | NR | |
| Wang 201834 | 38 (19.49) | 28 (14.36) | A: 12 (6.15%) B: 55 (28.21%) C: 61 128 (65.64%) |
NR | ≥3.03: 143 (73.33%) <3.03: 52 (26.67%) | ≥1: 73 (37.44%) <1: 122 (62.56%) | NR |
| 13 (6.67) | 3 (1.54) | A: 26 (13.33%) B: 78 (40%) C: 91 (46.67%) |
NR | ≥3.03: 113 (57.95%) <3.03: 82 (42.05%) | ≥1:101 (51.79%) <1: 94 (48.21%) | NR | |
| Wlazlo 201444 | NR | NR | 7.3 (2) | 11.8 (7.3) | 1.36 (0.97) | NR | NR |
| NR | NR | 7.8 (2.1) | 12.2 (7.5) | 2.4 (2.36) | NR | NR | |
| Xue 202245 | 19 (19.4) | 12 (12.2) | A: 3 (3.1%) B: 30 (30.6%) C: 65 (66.3%) |
NR | 4.02 (4.53) | 4.1 (3.66) | NR |
| 11 (6.5) | 12 (7.1) | A: 16 (9.4%) B: 83 (48.8%) C: 71 (41.8%) |
NR | 0.63 (0.33) | 1.23 (0.52) | NR | |
| Zhang 202046 | 5 (23.8) | 0 (0) | A: 1 (4.8%) B: 16 (76.2%) C: 4 (19%) |
NR | 1.83 (0.6) | NR | NR |
| 33 (17.1) | 9 (4.7) | A: 17 (8.8%) B: 105 (54.4%) C: 71 (36.8%) |
NR | 2.4 (0.54) | NR | NR | |
| Zuwala-Jagiello 201947 | NR | NR | 8.1 (2) | 12.8 (5.9) | Median (IQR): 2.3 (1.1-3.6) | NR | NR |
| NR | NR | 7.08 (2.1) | 11.2 (7.5) | Median (IQR): 1.5 (1-2) | NR | NR |
Age and MELD score are mean (standard deviation) except where indicated otherwise. DM: Diabetes mellitus; SBP: Spontaneous bacterial peritonitis; NR: Not reported; IQR: Interquartile range; SD: Standard deviation
Risk for spontaneous bacterial peritonitis
Thirteen studies reported the incidence of SBP in patients with DM versus patients without, with a total of 75 287 patients. The overall RR showed that DM significantly increased the risk of SBP (RR=1.26; 95% CI [1.05, 1.51]; P=.01). The pooled studies showed significant moderate heterogeneity (P<.001; I2=72%) (Figure 2). We performed a sensitivity analysis excluding one study for each scenario; however, the heterogeneity was not resolved by excluding any study. We additionally performed a sensitivity analysis excluding two studies for each scenario, and the heterogeneity was resolved by excluding Liu et al 201633 and Wang et al 201834 (P=.16; I2=31%). After resolving the heterogeneity, the overall RR still showed that DM significantly increased the risk of SBP; however, with more clear evidence and higher RR (RR=1.41; 95% CI [1.22, 1.63]; P<.001) (Figure 3).
Figure 2.
Forest plot comparing patients with DM versus patients without DM regarding the incidence of SBP.
Figure 3.
Forest plot comparing patients with DM versus patients without DM regarding the incidence of SBP after resolving the heterogeneity.
Pooled hazard ratios for developing spontaneous bacterial peritonitis in patients with diabetes
Eight studies reported the pooled hazard ratios for developing SBP in patients with DM. The overall HR showed that DM significantly increased the risk of SBP (HR=1.70; 95% CI [1.32, 2.18]; P<.001). The pooled studies showed no significant heterogeneity (P=.12; I2=39%) (Figure 4).
Figure 4.
Forest plot showing the pooled hazard ratio of developing SBP in patients with DM.
Publication bias assessment
Visual inspection of the funnel plot, which plots the standard error of the log risk ratios against the risk ratios themselves, did not reveal a pronounced asymmetry. The majority of studies were distributed symmetrically, suggesting no clear evidence of publication bias across the studies included in our meta-analysis. However, we observed a paucity of studies in the bottom left quadrant of the plot, indicating a potential underrepresentation of small studies with negative effects and higher standard errors (Figures 5 and 6).
Figure 5.
Funnel plot showing the publication bias among the included studies.
Figure 6.
Funnel plot showing the publication bias among the included studies.
To substantiate the funnel plot's findings, further statistical tests such as the Failsafe-N test, Kendall's Tau rank correlation test, and regression test for funnel plot asymmetry test for publication bias were conducted. The Failsafe-N analysis, using the Rosenthal approach, yielded a value of 47 (P<.001). This indicates that an additional 47 null-effect studies would be needed to bring the P value of our overall effect size above the threshold for statistical significance. Kendall's Tau rank correlation test yielded a Tau value of .051 with a P value of .858. This non-significant result suggests there is no strong evidence of a correlation between study effect sizes and their variances, which implies that publication bias is not a significant concern in our dataset. The regression test for funnel plot asymmetry provided a Z-score of 0.150 with a P value of .880. Similar to Kendall's Tau, this non-significant result indicates no evidence of asymmetry in the funnel plot, supporting the absence of publication bias (Supplementary Table 5).
DISCUSSION
Significance of the study and summary of findings
The significance of our study lies in its comprehensive exploration of the association between DM and the risk of SBP in cirrhotic patients. With a meticulous literature search yielding 416 records and the subsequent inclusion of 15 studies in the meta-analysis, we present a robust synthesis of the evidence. The study's importance is underscored by the substantial number of patients (76 962) with liver cirrhosis included in our analysis.
Our meta-analysis revealed a clear and statistically significant association between DM and an increased risk of SBP in cirrhotic patients. The overall RR indicated a 26% higher risk of SBP in patients with DM (RR= 1.26; 95% CI [1.05, 1.51]; P=.01). Sensitivity analyses, although performed, did not resolve the observed heterogeneity, ultimately reinforcing the robustness of our findings. Furthermore, hazard ratios for developing SBP in patients with DM were consistently elevated (HR=1.70; 95% CI [1.32, 2.18]; P<.001), supporting a heightened risk associated with diabetes.
Explanation of the findings
The observed association between DM and increased risk of SBP suggests a complex pathophysiological interplay. DM is known to impair the immune system, particularly neutrophil function, which is crucial in combating bacterial infections like SBP.16–18 Additionally, DM might exacerbate liver dysfunction in cirrhosis,9,13 leading to increased intestinal permeability and bacterial translocation,33 which are key factors in the development of SBP.1,2,5 The chronic inflammatory state and altered gut microbiota associated with DM could further predispose these patients to SBP.39–41
However, describing the severity of cirrhosis is very important to assess the actual impact of DM. Bossen et al40 investigated the impact of DM on the risk of SBP in cirrhotic patients and found that DM has no significant impact when adjusted to the severity of cirrhosis. They attributed this to the fact that when cirrhosis reaches its most severe, decompensated stage, it presents such a significant risk of infection that any additional risk posed by diabetes becomes challenging to identify.40 Additionally, it is crucial to evaluate the glycemic state of the patients and classify the diabetic patients according to the severity of DM. Poor control of glycemia in both type 1 and type 2 diabetes is associated with an elevated risk of infection.19,34,43 One of the included studies (Tergast et al)39 evaluated the glycemic state on the incidence of SBP in diabetic patients. The authors found that the incidence of SBP was higher in diabetic patients with HbA1c ≥6.4% compared to patients with controlled DM (HbA1c <6.4%).44 Additionally, they found that there was no increase in the risk of SBP in patients with controlled DM compared to non-diabetics. DM also significantly heightens the risk of hepatocellular carcinoma (HCC), a primary malignancy of the liver.48–50 Epidemiological evidence suggests a strong association between DM and HCC, with DM patients exhibiting a notably elevated risk of developing HCC compared to non-diabetic individuals.51,52 Moreover, emerging research indicates that DM may not only increase the incidence but also accelerate the progression of HCC in affected patients.51,53 The underlying mechanisms linking DM to HCC are multifactorial, encompassing insulin resistance, chronic inflammation, aberrant lipid metabolism, and the dysregulation of various signaling pathways implicated in hepatocarcinogenesis.54
Implications and recommendations of these findings for practice
Because SBP is associated with increased hospitalizations, escalated healthcare costs, a substantial decline in quality of life, and an increased mortality rate of up to 17.6% in cirrhotic patients,27,45 preventing complications, including SBP, is crucial. The main preventive measures for SBP primarily involve the use of antibiotic prophylaxis, especially for patients who have previously experienced an episode of SBP or have low protein levels in their ascitic fluid.5,6,46,47,55,56 Widely recommended antibiotics for this purpose comprise norfloxacin, ciprofloxacin, and trimethoprim-sulfamethoxazole.47,55 Furthermore, it is frequently advised to implement primary prevention measures, such as in individuals with advanced liver disease, alongside antibiotic prophylaxis. This entails systematic monitoring and timely identification methods, which encompass periodic analysis of ascitic fluid.57 Additionally, the proficient handling of underlying conditions linked to cirrhosis, such as hepatitis B and C, alcohol use disorder, and nonalcoholic steatohepatitis, is essential in diminishing the risk of SBP.2,5,46,57 The results of our study, which demonstrate a 26% higher risk of SBP in cirrhotic patients with DM, are crucial in informing the implementation of preventive measures. These findings emphasize the need to possibly adjust the criteria for starting preventive antibiotics, especially in diabetic patients with cirrhosis. Maintaining a healthy blood sugar level is still very important, but people with DM may benefit from starting antibiotics earlier or using a stronger treatment plan, since they are more likely to have complications.
Furthermore, the heightened susceptibility to SBP in diabetic patients with liver cirrhosis necessitates increased vigilance from healthcare providers and patients alike. Providing comprehensive information to all individuals involved about this heightened risk and the indications and manifestations of SBP can result in prompt identification and management, potentially enhancing patient results. Our findings suggest that further investigation into the specific needs of diabetic patients with liver cirrhosis is crucial for optimizing SBP prevention strategies. This includes exploring the potential benefits of both primary and secondary prophylaxis in this population. The insights gained from such research may inform future updates to relevant clinical protocols for managing liver cirrhosis.
Strengths and limitations
Our study's major strength lies in its comprehensive data collection from a large number of studies, encompassing a significant patient population. This provides a robust dataset that strengthens the reliability of our conclusions. Our results are robust due to the statistical rigor incorporated into our meta-analysis, specifically through the implementation of sensitivity analyses and the evaluation of publication bias. Furthermore, by quantifying the risk of SBP in patients with DM who have liver cirrhosis, our study addresses a critical gap in the existing literature and provides novel insights that are highly applicable to clinical practice.
However, our study is not without limitations. Many of the included studies were retrospective in nature, which may have a selection, survivorship, or confounding bias. There's also potential variability due to differences in diagnostic criteria, patient populations, and treatment protocols across the included studies. This may introduce a degree of heterogeneity that could affect the generalizability of our findings. Additionally, only one of the included studies evaluated the impact of the control of diabetes on the risk of SBP. Also, we could not perform subgroup analysis according to the cause of liver cirrhosis, Child and MELD classifications because of insufficient data in the included studies.
Recommendations for future research
In light of our findings, future research should delve into the mechanisms underlying the observed association, considering factors influencing heterogeneity. Future research should also focus on establishing causal relationships between DM and SBP in patients with liver cirrhosis using prospective cohort studies or randomized controlled trials. It is critical to investigate the underlying mechanisms, such as immune system changes and gut microbiota changes. Evaluating the efficacy of various intervention strategies for SBP prevention and management in this specific patient population would provide useful insights. Furthermore, researching long-term outcomes and conducting studies in a variety of geographic and healthcare settings would help us better understand global variations in SBP risk and management. We also recommend future studies to classify diabetic patients according to the severity of DM and according to the severity of cirrhosis to find the actual interplay between both conditions. Also, we suggest future research to evaluate sub-analyses regarding the cause of liver cirrhosis (viral vs non-viral and alcoholic vs non-alcoholic etiology, Child B vs Child C patients, MELD <15 vs >15, diabetic plus prediabetic patients vs diabetic patients only, and well-controlled diabetes vs. not well-controlled diabetes.
In conclusion, our meta-analysis provides solid evidence of the increased risk of SBP in cirrhotic patients with DM. These findings not only highlight DM as an independent risk factor for SBP but also underscore the critical need for targeted preventive measures in this population. Our study advocates for the integration of vigilant SBP monitoring and proactive DM management into clinical practice, emphasizing the revision of current guidelines. Acknowledging the limitations of retrospective analyses, we call for further research to enhance our understanding and improve outcomes for these high-risk patients.
SUPPLEMENTS
Supplementary Table 1.
The detailed search strategy and results for each database.
| Database | Search strategy | Results |
|---|---|---|
| PubMed | (Diabetes Mellitus OR Diabetes OR DM) AND (Spontaneous Bacterial Peritonitis OR Primary Bacterial Peritonitis) | 121 |
| Scopus | (“Diabetes Mellitus” OR “Diabetes” OR “DM”) AND (“Spontaneous Bacterial Peritonitis” OR “Primary Bacterial Peritonitis”) | 114 |
| Web of Science | (Diabetes Mellitus OR Diabetes OR DM) AND (Spontaneous Bacterial Peritonitis OR Primary Bacterial Peritonitis OR Bacterial Peritonitis) | 128 |
| Cochrane Library | (Diabetes Mellitus OR Diabetes OR DM) AND (Spontaneous Bacterial Peritonitis OR Primary Bacterial Peritonitis OR Bacterial Peritonitis) | 53 |
Supplementary Table 2.
Risk of bias according to the Newcastle Ottawa scale tool for the included case-control studies.
| Selection | Comparability | Exposure | Overall score | ||||||
|---|---|---|---|---|---|---|---|---|---|
| Definition of cases | Representativeness of the cases | Selection of Controls | Definition of Controls | Ascertainment of exposure | The same method of ascertainment for cases and controls | Non-Response rate | |||
| Bajaj 2009 | * | * | * | * | * | * | * | * | 8 |
| Campbell 2007 | * | * | * | * | ** | * | * | * | 9 |
Supplementary Table 3.
Risk of bias according to the Newcastle Ottawa scale tool for the included cohort studies.
| Selection | Comparability | Outcome | Overall score | ||||||
|---|---|---|---|---|---|---|---|---|---|
| Representativeness of the exposed cohort | Selection of the nonexposed cohort | Ascertainment of exposure | Demonstration that the outcome of interest was not present at the start of the study | Comparability of cohorts on the basis of the design or analysis | Assessment of outcome | Was followup long enough for outcomes to occur | Adequacy of followup of cohorts | ||
| Bossen 2019 | * | * | * | * | ** | * | * | 8 | |
| Liu 2016 | * | * | * | * | ** | * | * | * | 9 |
| Tergast 2018 | * | * | * | * | ** | * | * | * | 9 |
| Tu 2020 | * | * | * | * | ** | * | * | * | 9 |
| Wang 2018 | * | * | * | * | ** | * | * | * | 9 |
| Wlazlo2014 | * | * | * | * | ** | * | * | * | 9 |
| Xue 2022 | * | * | * | * | ** | * | * | * | 9 |
| Zhang 2020 | * | * | * | ** | * | * | * | 8 | |
| Zuwala-Jagiello 2019 | * | * | * | * | ** | * | * | * | 9 |
Supplementary Table 4.
Risk of bias according to the Newcastle Ottawa scale tool for the included cohort studies.
| Selection | Comparability | Outcome | Overall score | |||||
|---|---|---|---|---|---|---|---|---|
| Representativeness of the sample | Sample size | Non-respondents | Ascertainment of exposure | Assessment of the outcome | Statistical test | |||
| Kraja 2012 | * | * | ** | * | * | * | 7 | |
| Mohammad 2016 | * | * | ** | ** | * | * | 8 | |
| Sigal 2006 | * | * | ** | * | * | * | 7 | |
| Thiele 2014 | * | * | ** | ** | * | * | 8 | |
Supplementary Table 5.
The publication bias test results.
| Test name | Test statistics | P value |
|---|---|---|
| Failsafe-N analysis (file drawer analysis) | 47.000 | <.001 |
| Rank correlation test for funnel plot asymmetry | Kendall's Tau = 0.051 | .858 |
| Regression test for funnel plot asymmetry | Z= 0.150 | .880 |
Funding Statement
None.
PROTOCOL REGISTRATION
The protocol was prespecified and registered on the International Prospective Register of Systematic Reviews (PROSPERO) with ID (CRD42024497609)
FUNDING
No funding was received for this research.
CONFLICT OF INTEREST
None.
DATA AVAILABILITY
All required data and materials are available upon request.
ETHICAL APPROVAL
None of the authors were investigators in the studies in this meta-analysis.
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Associated Data
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Data Availability Statement
All required data and materials are available upon request.