Introduction

Worldwide, CRC accounts for 10% of all cancer cases and is the second most prevalent cancer-related killer [1]. Predictions show that colon cancer mortality will rise by 60% by 2035 and rectal cancer mortality by 71.5% [2]. Current estimates place CRC at number two on the list of cancer killers in the US by 2024, with males less than 50 years old being hit particularly hard by the disease [3].

Rigid resection is still the mainstay of CRC therapeutic regimens across the US, EU, UK, and China [4,5,6,7]. However, Severe complications such as anastomotic leakage (Incidence rate ranges from 5–19%) [8], bleeding (Incidence rate ranges from 1.3–1.5%) [9], and SSI (SSI, incidence rate ranges from 23–26%) [10, 11], continue to impact short-term outcomes by increasing postoperative mortality, prolonging hospital stay (LOS), escalating medical costs, and causing greater patient suffering [12]. Furthermore, postoperative complications after radical resection of CRC are known to affect long-term oncological outcomes [13].

Postoperative problems might arise from immunosuppression, which can be caused by factors such as tumor load, surgical stress, and malnutrition [14]. Patients having surgery for gastrointestinal cancer may benefit from perioperative immunnutrition since it lessens the likelihood of postoperative problems and decreases the length of time they spend in the hospital, according to recent studies [15, 16]. Immunonutrition formulations rely on arginine because of its important function in immune function modulation [17]. A large body of research dating back to the 1990s indicates that arginine supplementation during perioperative times improves immune function and reduces CRC patient complications [18,19,20]. However, a new randomized controlled trial with 176 colon cancer patients found that supplementing with arginine before surgery did not improve the results of the patient’s treatments [21].

Consolidating these evidences has been difficult due to the diversity in Research Designs, methodology, demographies, and sample sizes, found in existing clinical trials. To put these questions to rest and determine arginine’s actual therapeutic value for CRC patients having radical surgery, researchers conducted a meta-analysis that drew from randomized, prospective clinical studies. Important new information regarding the role of arginine in immune function and postoperative complications in CRC patients was uncovered in this meta-analysis.

Materials and methods

Protocol registration

The protocol was registered with PROSPERO in March 2024 (registration number: CRD42024520509).

Eligibility criteria

In accordance with the PRISMA and Cochrane Handbook for Systematic Reviews of Interventions, this meta-analysis was carried out. Research was considered for inclusion if it met the following criteria: (1) was a published randomized controlled trial, (2) included patients with a colon or rectal cancer diagnosis who underwent radical surgery, (3) included an intervention group that received arginine-enhanced immunonutrition during the perioperative period and a control group that received routine nutritional support, (4) reported on at least one of the outcomes under investigation. Studies were excluded if they: (1) were duplicate studies, (2) were irrelevant, (3) were laboratory studies, (4) were animal studies, (5) lacked accessible data or the original text, (6) were published in languages other than English or Chinese.

Search methodology

Two researchers worked separately to search all available databases (up to March 10, 2024) for relevant articles: PubMed, Cochrane Library, Web of science, MEDLINE, EMBASE, Wanfang, CNKI, CBM, VIP, etc. The following terms were used in the search: (colon OR rectal OR colorectal) AND (cancer OR tumor OR carcinoma OR neoplasm) AND (arginine OR argininosuccinic acid OR immunonutrition OR immunomodulatory) AND (random OR randomized OR RCTs OR clinical trial). A combination of MeSH and free-text information was also considered. The detailed search strategies were provided in Supplement 1.

Study selection

A thorough examination of the complete texts of selected publications followed the screening of possibly relevant literature by reviewing titles and abstracts. The inclusion or deletion of a certain article was decided upon according to the established criteria. Two evaluators, Zan Ouyang and Ping Chen, independently assessed each article’s research design, study subjects, implementation plan, and results according to the established criteria and collected pertinent information. In cases of disagreement, a third party was consulted.

Data extraction and analysis

Zan Ouyang and Li Zhou, working separately, retrieved the following data: (i) the name and publication year of the first author, (ii) the enrollment total of patients and their corresponding age ranges/distributions were recorded; (iii) the experimental and controlled treatment plans; (iv) primary and secondary observational indicators, including IgA, IgG, IgM, CD4+, CD8+, and the CD4+/CD8+ ratio, and (v) Complications after surgery, namely anastomotic leakage, LOS, and SSI. We talked things out and, if that didn’t work, we brought in an outsider.

Quality assessment

To determine how well the included RCTs performed, researchers used the Cochrane Collaboration’s bias assessment method. This evaluation paid special attention to the following critical areas: the production of random sequences, the concealment of allocations, the blinding of participants and staff, the blinding of outcome assessment, the management of incomplete outcome data, selective reporting, and other possible biases.

Statistical analysis

The statistical analysis was conducted using Stata (StataCorp, version 14.0), with a 95% confidence interval (CI) employed for estimation. Continuous data from laboratory tests and LOS were shown as mean ± standard deviation, whereas dichotomous variables like SSI and anastomotic leakage were calculated as relative risks (RR), and forest plots were presented as risk difference (RD). A high level of homogeneity was indicated by P<0.1 and I2<50%, respectively, when evaluating the heterogeneity among the included studies, whereas a high level of heterogeneity was denoted by the contrary. A random-effects model with heterogeneity analysis was utilized in heterogeneous studies, while a fixed-effects model was utilized in homogeneous studies. To check for publication bias, we utilized a funnel plot, and to see how high-risk papers affected the meta-analysis as a whole, we ran a sensitivity analysis.

Results

Study selection outcome

There were 681 relevant articles found, with 560 being duplicates. Review papers, conference abstracts, animal studies, and case reports accounted for 77 of the 77 items that were discarded after abstract and title screening. This left 44 publications for further consideration. Our full-text examination revealed that 12 publications lacked suitable control groups, 3 were inaccessible, and 5 had data that could not be extracted. As a result, all of these articles were removed. After careful review, a total of 24 publications were ultimately deemed suitable for inclusion in the meta-analysis. Figure 1 depicts the retrieval procedure.

Fig. 1
figure 1

Literature search and Filtering Diagram

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Study characteristics

You may find a summary of the 24 studies’ features in Table 1. From 1999 to 2024, a grand total of 1,883 individuals were involved in the investigations; 905 were assigned to the experimental group and 978 were assigned to the control group. Ten studies utilized enteral nutrition (EN), while 14 employed parenteral nutrition (PN). Of these, 20 detailed the specific use of arginine, while 4 did not specify the arginine dosage. 13 studies focused on patients with CRC, and 10 specifically targeted colon cancer. Regarding immune parameters, 13 trials (18,20,24,25,26,27,29,31,34,36,38,40,41) reported CD4+ T cell content, 11 reported CD8+ T cell content (18,25,26,27,29,30,34,36,38,40,41), and 14 reported the CD4+/CD8+ ratio in peripheral blood (18,25,26,27,29,30,31,32,34,36,38,39,40,41). For humoral immunity, 13 trials reported IgA levels (18,23,25,26,27,29,30,31,32,33,36,38,41), 14 (18,23,25,26,27,29,30,31,32,33,36,38,40,41) reported IgM levels, and another 14 (18,23,25,26,27,29,30,31,32,33,36,38,40,41) reported IgG levels in peripheral blood. Additionally, anastomotic leakage was addressed in 11 trials (19,21,22,23,24,28,30,32,33,35,37), SSI in 12 trials (18,19,21,22,23,24,28,30,32,33,35,37), and length of hospital stay in 10 trials (19,21,22,23,24,28,30,32,35,37).

Table 1 Main characteristics of included studies
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Study quality assessment

Two researchers worked separately to do the quality assessments in Review Manager 5.3. To examine the included RCTs’ methodological quality, they used the bias risk assessment tool offered by the Cochrane Collaboration. The results are presented in Fig. 2A and B. Of the 24 RCTs, two did not specify whether random grouping was used, resulting in a high-risk rating. Another 12 articles did not clearly describe the method for generating the random sequence, which led to an unclear risk assessment. However, the remaining 10 RCTs thoroughly described their methods for generating random sequences. None of the included RCTs used blinding procedures, and the allocation concealment was unclear. There was no evidence of biased reporting or missing data in any of the research.

The Cochrane Collaboration Network GRADE was employed to assess the quality of evidence for this analysis. The results of the evaluation of these ten indicators showed that the certainty levels for CD8 + and PA were very low, levels for IgA, IgM, IgG, CD3 + and CD4+/CD8 + were low, and CD4+, TP, ALB were moderate. There are potential reasons for the downgrade: (1) The included studies had significant variations in randomization, allocation concealment, and blinding; (2) The sample size included in the original studies was limited; (3) Significant heterogeneity between studies. The downgrade is a certain degree indicating the publication bias of included studies, the positive results were published publicly or potential negative results were not reported which results in publication bias and downgrading of the level of evidence. The results of GRADE were provided in Supplement 2.

Fig. 2
figure 2

The quality of the included RCTs in terms of methodology: A Summary of bias risk. B Potential bias

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Results of meta-analysis

Effect of arginine on humoral immunity of patients with CRC

After conducting the heterogeneity test, it was decided whether the combined analysis of humoral immune function indicators, specifically IgA, IgG, and IgM, should be performed using a fixed-effects model or a random-effects model. In Fig. 3, you can see the outcomes. Thirteen studies reporting IgA levels involving seven hundred fifty participants were included in the analysis using a fixed-effects model (I2 = 29.9%, P = 0.145). Figure 3A shows that there was a significant difference in IgA levels between the arginine group and the control group (Z = 6.04, p < 0.001, SMD = 0.45, 95% CI: 0.30–0.60). Out of 14 randomized controlled trials, 810 individuals reported their IgG levels. Figure 3B shows that the arginine group had significantly greater IgG levels compared to the control group, using a fixed-effects model (I²=14.9%, P = 0.291). The statistical analysis yielded a Z = 10.82, p < 0.001, SMD = 0.80, 95% CI: 0.64–0.96.14 randomized controlled trials with 810 participants reported IgM levels. Figure 3C shows that the arginine group had significantly greater IgM levels compared to the control group, as revealed by a random-effects model (I²=69.3%, p < 0.001), SMD = 0.66, 95% CI: 0.39–0.93, and Z = 8.95, p < 0.001.These findings suggest that arginine improves humoral immune function after radical surgery for CRC patients.

Fig. 3
figure 3

SMD forest plot for IgA, IgM, and IgG. A: IgA forest plot; B: IgG forest plot; C: IgM forest plot

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Effect of arginine on cellular immunity in patients with CRC

We investigated for heterogeneity to choose the right model before pooling the indications of cellular immune function, which include CD4+ T cells, CD8+ T cells, and the CD4+/CD8+ ratio. Figure 4 displays the results. Thirteen studies that reported on CD4+ T cell counts and included 589 people were analyzed using a random-effects model (I2 = 74.4%, P < 0.001). Figure 4A shows that compared to the control group, the arginine group had significantly increased CD4+ T cell numbers (Z = 11.22, p < 0.001, SMD = 1.03, 95% CI: 0.67–1.38). Eleven randomized controlled trials (RCTs) examined CD8+ T cell counts in 536 individuals. The study revealed that the arginine group had significantly reduced CD8+ T cell counts compared to the control group, using a random-effects model (I2 = 77.2%, P < 0.001) (Z = 7.87, p < 0.001, SMD=-0.70, 95% CI: -1.09 to -0.32; Fig. 4B). The CD4+/CD8+ ratio was reported in 14 RCTs that included 796 individuals. The results of the random-effects model, which demonstrated a significantly higher CD4+/CD8+ ratio in the arginine group compared to the control group (Z = 13.07, p < 0.001, SMD = 1.37, 95% CI: 0.88–1.86; Fig. 4C), were presented. According to these results, arginine controls cellular immune function in CRC patients after severe surgery.

Fig. 4
figure 4

SMD forest plot for CD4+, CD8+, and CD4+/CD8+ cells: Forest plots of CD4+, CD8+, and CD4+/CD8+ are shown in (A), (B), and (C) respectively

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Effect of arginine on post-operative complications and LOS in patients with CRC

To make sure the results were reliable, we checked for heterogeneity among the studies before doing the summary analysis. Figure 5 displays the outcomes. Twelve research examining SSI rates with 1,239 participants utilized a fixed-effects model (I2 = 0%, P = 0.825). Comparing the arginine and control groups, we find that the former had reduced SSI rates (Z = 3.31, P = 0.001, RR = -0.02, 95% CI: -0.05-0.00; Fig. 5A). Anastomotic leakage rates were reported in eleven RCTs in a total of 1,180 participants. Anastomotic leakage was shown to be less common in the arginine group compared to the control group, according to a fixed-effects model (I2 = 8.0%, P = 0.367; Fig. 5B). The P-value was 0.039, and the SMD was − 0.05. On LOS, ten randomized controlled trials documented 1,160 individuals. Based on the results from a random-effects model (I2 = 71.0%, P < 0.001), the arginine group exhibited a shorter length of stay (LOS) compared to the control group, as depicted in Fig. 5C. The statistical analysis indicated a significant difference, with a Z-score of 2.58, a p-value of 0.010, a standardized mean difference (SMD) of -0.15, and a 95% confidence interval (CI) ranging from − 0.38 to 0.08.

Fig. 5
figure 5

Anastomotic leakage, SSI, and LOS forest plot: The forest plots of SSI, anastomotic leakage, and LOS are shown in (A, B, and C, respectively). A SSI and a LOS are medical terms used interchangeably

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Robustness assessment for the sensitivity of pooled analysis

Sensitivity analysis

The pooled results for IgM, CD4+, CD8+, CD4+/CD8+, and LOS showed high heterogeneity (≥ 50%) across the included studies. Thus, a leave-one-out sensitivity analysis was performed to evaluate their robustness. Figure 6 displays the results. Excluding any one trial had no discernible effect on the heterogeneity, according to the sensitivity analysis for CD4+ outcomes (Fig. 6A), suggesting that the combined CD4+ results were robust. The sensitivity analyses of CD8+, CD4+/CD8+, IgM, and LOS all came to similar conclusions, demonstrating that these combined findings are robust (Fig. 6B, C and D, and 6E, respectively).

Fig. 6
figure 6

Analysis of sensitivity: A CD4+ sensitivity, B CD8+ sensitivity, C CD4+/CD8+ sensitivity, D IgM sensitivity, and E LOS sensitivity, duration of hospital stay

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Meta-regression analysis

The main outcomes, which include IgM, CD4+, CD8+, and CD4+/CD8+, showed substantial variation across investigations. As a result, we applied meta-regression analysis to find out where the differences were coming from and how much of an effect confounding variables had on the reliability of our pooled results. We examined the route of arginine administration, tumor kind, sample size, and duration of treatment as possible influential confounding factors after consulting and discussing the matter. In Table 2, the outcomes of the meta-regression were presented. Univariate and multivariate analysis revealed the administration route (PN or EN) of arginine had a significant influence on the results of CD4+, which indicated the heterogeneity may originate from this covariate. The remaining variable had no significant influence on the pooled effects of CD8+, CD4+/CD8+ and IgM in either the single or multi-factor regression analyses.

Table 2 Results of Meta-regression analysis
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Test of publication bias

The inclusion of RCTs was evaluated for publication bias using Egger’s test, and the results can be shown in Fig. 7. The following findings have been deduced from Egger’s test: A p-value of 0.75 and a coefficient of 0.77 are shown for CD4+ in Fig. 7A. No publication bias has been identified because the P-value is greater than the stated significance level of 0.05. The same conclusion applies to the other six Egger’s tests: For CD8+ (Fig. 7B), P = 0.86, Coefficient = 0.51. For IGA (Fig. 7D), P = 0.73, Coefficient=-0.43. For IGG (Fig. 7E), P = 0.66, Coefficient = 0.49. For IGM (Fig. 7F), P = 0.90, Coefficient = 0.24. For SSI (Fig. 7G), P = 0.59, Coefficient = 0.22. For LOS (Fig. 7H), P = 0.91, Coefficient = 0.24. In all cases, the P-values exceed 0.05, indicating no significant publication bias. However, Egger’s test result for CD4+/CD8+ (Fig. 7C) showed a P-value of 0.018, with a coefficient of 5.57 and a 95% CI ranging from 1.15 to 9.98, indicating significant publication bias. Following this, we re-examined the original studies and hypothesized that factors such as small sample sizes, intention-to-treat (ITT) analysis, and the lack of blinding in many studies might have influenced these biases. These factors could potentially impact the conclusions regarding CD4+/CD8+.

Fig. 7
figure 7

Egger’s publication bias plot for: A CD4+; B CD8+; C CD4+/CD8+; D IgA; E IgG; F Ig; (G)SSI; H LOS

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Discussion

The amino acid arginine is considered conditionally essential and plays a pivotal role in various metabolic processes within the human body. These processes include: (1) Participation in the urea cycle and facilitation of nitrogen-containing waste transportation [42, 43], (2) involvement as an intermediate in protein synthesis across diverse types within the body [42, 43], (3) serving as a precursor for polyamine and hydroxyproline synthesis, both crucial for repair mechanisms [44], (4) acting as a precursor in nitric oxide (NO) synthesis, a major vasodilator involved in intracellular signal transduction, stimulation of NK cell activation, and tumor growth inhibition [45, 46], (5) enhancing lymphocyte count in peripheral blood and exerting a positive regulatory effect on cellular immunity, primarily governed by T lymphocytes under specific conditions [47]. The exact cause of the abnormally low arginine levels seen in trauma, surgical procedures, and tumor patients is still unclear [48], but one possible explanation is the presence of type I arginase, an enzyme that breaks down arginine into ornithine, expressed by immature bone marrow primitive cells in the lymphatic and circulatory systems [49]. In colon cancer patients who have undergone radical surgery, the risk of infection is greatly increased due to the enormous depletion of arginine, which greatly hinders cellular immune activity [50, 51].

In patients undergoing radical resection for CRC, this meta-analysis confirms the impact of perioperative arginine supplementation on immune function and postoperative outcomes from three angles. At the outset, arginine significantly enhances humoral immune function following CRC radical resection. To add to that, it improves the cellular immune function of the patients. Thirdly, it keeps patients out of the hospital for shorter periods of time and reduces the risk of anastomotic leaks and infections at the surgical site. With respect to humoral immunity, three important markers showed notable improvements in the arginine group: IgA (SMD = 0.45, 95% CI: 0.30–0.60), IgG (SMD = 0.80, 95% CI: 0.64–0.96), and IgM (SMD = 0.66, 95% CI: 0.39–0.93). In terms of cellular immune function, pooled analysis revealed that the arginine group had significantly higher CD4+T cell counts (SMD = 1.03, 95% CI: 0.67–1.38) and CD4+/CD8+ ratios (SMD = 1.37, 95% CI: 0.88–1.86) compared to the control group, while the CD8+T cell count decreased significantly (SMD=-0.70, 95% CI: -1.09 to -0.32). These results show that the arginine group had significantly improved cellular immune activity. In addition, when looking at the data as a whole, it was found that the arginine group had a lower rate of SSIs (RR=-0.02, 95% CI: -0.05-0), a shorter LOS (SMD=-0.15, 95% CI: -0.38-0.08), and a significantly lower incidence of anastomotic leakage (SMD=-0.05, 95% CI: -0.08 to -0.02) compared to the control group.

The lack of arginine, as a central factor causing immunosuppression in CRC patients after radical surgery, warrants greater attention from clinicians and researchers. This study found that arginine improved immune function and decreased postoperative infection problems in CRC patients following radical surgery. Nevertheless, it is important to highlight the potential limitations of this integrated analysis. All the studies included in the analysis were not designed with single or double blinding, which increases the risk of detection bias. Additionally, the results of the GRADE assessment indicated the presence of publication bias among the studies could downgrade the level of evidence. Undetected bias was also found in studies with small sample sizes and missing ITT analysis, which may have contributed to potential bias. Furthermore, the metaregression analysis, using both univariate and multivariate methods, identified the method of administering arginine as a potential factor contributing to significant heterogeneity, casting doubt on the validity of the results in this combined analysis.