SYSTEMATIC REVIEW article

Front. Nutr., 09 September 2024

Sec. Nutritional Immunology

Volume 11 - 2024 | https://doi.org/10.3389/fnut.2024.1452338

Comparative efficacy of different single drugs to prevent necrotizing enterocolitis in preterm infants: an update systematic review and network meta-analysis

  • 1. Department of Neonatology, The First People’s Hospital of Neijiang, Neijiang, China

  • 2. Department of Orthopedics, The First People’s Hospital of Neijiang, Neijiang, China

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Abstract

Objective:

To investigate an optimal regimen of six drugs, including lactoferrin, probiotics, prebiotics, glutamine, arginine and erythropoietin (EPO), for the prevention of necrotizing enterocolitis (NEC) in preterm infants.

Methods:

PubMed, Embase, Ovid, The Cochrane Library, and Web of Science databases were searched for randomized controlled trials (RCTs) investigating the efficacy of lactoferrin, probiotics, prebiotics, glutamine, arginine, and EPO in preventing NEC in preterm infants, with a cutoff date of June 20, 2024. Two authors independently screened studies and extracted all the data. Network meta-analysis (NMA) was conducted to compare the outcomes of different interventions, and group rankings were determined using the surface under the cumulative ranking curve (SUCRA).

Results:

A total of 89 RCTs with 26,861 preterm infants were included. Arginine demonstrated the highest clinical efficacy in reducing the incidence of NEC, with probiotics being the next most effective and the placebo being the least effective. Lactoferrin was identified as the most effective intervention for reducing the incidence of NEC-associated sepsis. Prebiotics showed the highest effect on overall mortality, reducing the beginning of enteral feeding, and were associated with the shortest hospital stay. Glutamine significantly decreased the time to full enteral feeding.

Conclusion:

Existing literature highlights arginine as the most efficacious pharmacological agent in preventing NEC in preterm infants. It has been shown to effectively lower the rates of NEC, septicemia, and mortality, warranting its recommendation as the first-line clinical intervention. Following this, probiotics are recommended as a second option.

1 Introduction

Necrotizing enterocolitis (NEC) is among the most prevalent critical conditions affecting premature infants (13), found in 5–12% of very low birth weight (VLBW) infants (46). It presents with necrosis of the intestinal tissues in small and large bowels, which leads to a translocation of gut microbiota into the bloodstream and can also lead to sepsis (710). In general, in stage II, or definitive disease, there is nearly always evidence for pneumatosis intestinalis and/or portal venous gas (3, 11). Mortality rates among neonates requiring surgery are estimated to be 20–30% (3). Beyond the high mortality, NEC also carries a significant risk of morbidity in survivors, manifesting as short bowel syndrome and developmental stagnation (12). The complexity of NEC lies in its resistance to intervention once fully established, compounded by the scarcity and expense of treatment options. Use of antibiotics, gastric decompression, and parenteral nutrition are the most common (9). The etiology of NEC remains elusive, with the debate ongoing on whether it constitutes a single pathological entity or a spectrum of related disorders. Despite advancements in deciphering its pathophysiological mechanisms, substantial gaps in knowledge persist, potentially accounting for the stagnant progress in NEC therapeutics over recent decades (13). Consequently, NEC prevention is underscored as a vital strategy to mitigate premature infant mortality and morbidity rates.

Breastfeeding is recognized as a safe and effective preventive approach for NEC in preterm infants (14, 15); yet, the role of other adjunctive medications or additives is also significant. For example, probiotics, prebiotics, glutamine, arginine, lactoferrin, and EPO have been studied as a therapy to decrease the risk of NEC among preterm infants (1622). While initial data have suggested that probiotics can reduce the incidence and mortality of NEC (2325), efficacy and potential short-term or long-term side effects of the other therapies remain unclear. Given the unique characteristics of the gastrointestinal (GI) tract in preterm infants, the concurrent use of multiple additives is generally discouraged.

Network meta-analysis (NMA) compares three or more interventions simultaneously in a single analysis by combining direct and indirect evidence across a network of studies (26). The major advantage over traditional meta-analysis is that this approach integrates direct and indirect data, enabling a comprehensive comparison and efficacy ranking of multiple interventions to identify the optimal strategy (27).

This study employed NMA to assess and rank the preventive and therapeutic effects of probiotics, prebiotics, glutamine, arginine, lactoferrin, and EPO on NEC in preterm infants, intending to provide valuable evidence-based medical evidence for drug selection in future clinical practice.

2 Methods

2.1 Protocol and registration

This study adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement (28), ensuring a structured methodology and reporting format, and A Measurement Tool to Assess systematic Reviews (AMSTAR) 2 guidelines (29). The NMA protocol has been duly registered in the PROSPERO database (the registration number: CRD42024496947).

2.2 Data sources

A comprehensive literature search was conducted independently by two researchers (the first and second authors); disparities were resolved by discussion. The search encompassed titles and abstracts, and full-text assessments were carried out as needed to determine study eligibility.

The following databases were systematically searched from their inception until June 20, 2024: PubMed, Embase, Ovid, The Cochrane Library, and Web of Science. Placebo-controlled and head-to-head RCTs examining probiotics, prebiotics, glutamine, arginine, lactoferrin, and EPO as therapy against NEC in preterm infants were included. The following relevant terms were searched: (“enterocolitis necrotizing [MeSH Terms]” OR “necrotizing enterocolitis”) AND (“lactoferrin” OR “probiotics” OR “prebiotics” OR “glutamine” OR “arginine” OR “erythropoietin”). Additionally, Google Scholar was consulted to identify potentially relevant literature. Furthermore, the reference lists of identified reports were meticulously reviewed to identify any additional pertinent studies. Only articles published in the English language were considered for inclusion. The detailed search strategy is shown in Table 1 (PubMed is used as an example).

Table 1

#1 Enterocolitis necrotizing [MeSH Terms]
#2 Enterocolitis necrotizing [Title/Abstract]
#3 #1 OR #2
#4 Lactoferrin [MeSH Terms]
#5 Lactoferrin [Title/Abstract]
#6 Probiotics [MeSH Terms]
#7 Probiotics [Title/Abstract]
#8 Prebiotics [MeSH Terms]
#9 Prebiotics [Title/Abstract]
#10 Glutamine [MeSH Terms]
#11 Glutamine [Title/Abstract]
#12 Arginine [MeSH Terms]
#13 Arginine [Title/Abstract]
#14 Erythropoietin [MeSH Terms]
#15 Erythropoietin [Title/Abstract]
#16 #4 OR #5 OR #6 OR #7 OR #8 OR #9 OR #10 OR #11 OR #12 OR #13 OR #14 OR #15
#17 # 3 AND #16

Search strategy on PubMed.

2.3 Eligibility criteria

The inclusion criteria were as follows: (1) participants: Preterm infants born <34 weeks of gestation and/or infants with birth weight < 1,500 g; (2) types of studies: RCTs; (3) interventions: administration of early lactoferrin, probiotics, prebiotics, glutamine, arginine, erythropoietin and placebo (< 8 days of postnatal age) by any route and dose continued for any duration; each study involved at least two interventions; (4) Outcomes: primary outcomes: the incidence of NEC, NEC-associated sepsis and overall mortality; secondary outcomes: time to beginning enteral feeds, time to full enteral feeds and duration of hospitalization.

The exclusion criteria were: (1) non-RCTs, including quasi-RCTs, case–control studies, cohort studies, case reports, protocols, review articles, meta-analyses, editorials, letters, animal studies, cadaveric trials, or conference abstracts; (2) studies with <20 cases; (3) studies combining drugs (e.g., a combination of lactoferrin and probiotics); (4) poor-quality research literature or studies lacking rigor in their design; (5) duplicate or similar documents published by the same author in different journals; (6) incomplete data or important research data could not be obtained through email and other contacts; (7) non-English articles.

2.4 Data extraction

A specifically designed form was employed to extract essential information from each study. The following data were extracted: (1) general information such as the lead author, year of publication, study design, and country in which the study was performed; (2) demographic information, including the number and proportion of male or female infants, gestational age, birth weight, and the number of infants involved; (3) details regarding the drugs (intervention and comparison); (4) information on clinical outcomes, including the incidence of NEC, NEC-associated sepsis, overall mortality, beginning enteral feeding (time), full enteral feeding (time), and duration of hospitalization. In instances where SD was not available from the publication, SD was imputed using the method prescribed in the Cochrane Handbook, as follows:

  • 1. Obtaining SDs for a group of means were calculated from standard error of the mean (SEM) or 95% confidence intervals (CIs) by using equations from the Cochrane Handbook chapter 6.5.2.2 when the group SDs were not provided directly;

  • 2. When concentrations were provided in medians and 25th – 75th percentile, we converted these into means ± SD by using the equation developed by Wan et al. (Cochrane Handbook chapter 6.5.2.5);

  • 3. when not reported, change-from-baseline SDs were estimated using the equation developed by Follmann et al., assuming a correlation coefficient of 0.50 between baseline and post-intervention lipid and lipoprotein values [Cochrane Handbook chapter 6.5.2.8, 2].

2.5 Quality assessment

The Cochrane Risk of Bias Tool was employed to assess the quality. The risk of bias for the included trials was evaluated by two researchers based on the Cochrane Handbook criteria. The criteria covered randomization, allocation concealment, blinding of participants and personnel, blinding of outcome assessors, completeness of outcome data, selective reporting, and other biases. Each domain was classified as having an unclear risk, low risk, or high risk of bias. The assessment was deemed to be of high quality if most of the domains were well-described and exhibited a low risk of bias. In cases of discrepancies in the ratings, researchers reached a consensus through discussion.

2.6 Statistical analysis

To conduct a comprehensive NMA, we utilized the statistical software packages “Network” and “mvmeta” within STATA 17.0 software. Dichotomous variables, specifically the incidence of NEC, NEC-associated sepsis and overall mortality, were analyzed using relative risk (RR) with corresponding 95% confidence intervals (CI). Meanwhile, continuous variables, including time to beginning enteral feeds, time to full enteral feeds and duration of hospitalization, were analyzed using weighted mean differences (WMD) with corresponding 95% CI. The comparison was considered statistically non-significant when the 95% CI of the RR or WMD contained the value 1.

For direct comparisons, a conventional meta-analysis was conducted to aggregate the results using random-effects models, serving as sensitivity analyses. NMA employed a frequentist approach with a random-effects model to estimate direct and indirect comparisons. The primary objective of the NMA was to assess whether comparator interventions demonstrated superiority. Global inconsistency, local inconsistency (using a node-splitting approach), and loop inconsistency were used to evaluate potential inconsistencies between indirect and direct comparisons. Statistical significance for global inconsistency was determined using p-values, with p > 0.05 indicating no significant global inconsistency. Local inconsistency was assessed through node-splitting analysis, and p > 0.05 indicated no significant local inconsistency. Heterogeneity within each closed loop was estimated using the inconsistency factor (IF), with a 95% CI (IF) value of zero signifying no statistical significance. A global network diagram was employed in each pre-specified outcome to illustrate direct comparisons between interventions. The size of the nodes in the diagram corresponded to the number of participants receiving each treatment. Lines linked treatments subject to direct comparisons, and the thickness of these lines was proportional to the number of trials evaluating the specific comparison.

Within the “Results” section, the ranking probability of each intervention was presented using a cumulative probability ranking graph. The graph incorporated the Surface Under the Cumulative Ranking Curve (SUCRA) value, serving as an index summarizing the cumulative ranking probability. The SUCRA value ranged between 0 and 100%, where a larger SUCRA value indicated a higher ranking for the intervention, typically reflecting a more favorable or less favorable effect. All intervention measures were ranked based on their respective SUCRA values or the area under the curve, resulting in a comprehensive ranking of the interventions.

A comparison-adjusted funnel plot was used to assess the potential for publication bias. This analysis aimed to determine whether there was evidence of a small sample effect or publication bias within the intervention network.

3 Results

3.1 Search results

A total of 23,357 studies were initially identified, including PubMed (n = 350), Embase (n = 414), Ovid (n = 351), Web of Science (n = 606), and the Cochrane Library (n = 128) studies. To eliminate duplicate entries, the “Find duplicates” function in EndNote software was employed, removing 1,316 studies. After thoroughly screening titles and abstracts, 382 irrelevant references were excluded. Subsequently, a full text was retrieved for the remaining 151 references. Ultimately, 89 studies involving 26,861 neonates met the eligibility criteria for inclusion in this NMA. The study selection process is illustrated in Figure 1, and the baseline characteristics of the included studies are summarized in Table 2.

Figure 1

Figure 1

Flow diagram of the study selection process.

Table 2

Author Country Study design Group NO Gestational age (week) Birth weight (g) Outcome
Akin 2014 Turkey RCTs Lactoferrin 22 29.5 ± 1.6 1,290 ± 346.7 (1)(2)(3)
Placebo 23 30.3 ± 2.5 1,307 ± 262.1
Al-Hosni 2012 United States RCTs Probiotic 50 25.7 ± 1.4 778 ± 138 (1)(2)(3)
Placebo 51 25.7 ± 1.4 779 ± 126
Amin 2002 Canada RCTs L-arginine 75 27.4 ± 0.3 952 ± 25 (1)(2)
Placebo 77 27.6 ± 0.2 955 ± 20
Armanian 2014 Iran RCTs Prebiotic 25 30.48 ± 2.31 1262.80 ± 213.35 (1)(2)(3)(4)(5)(6)
Placebo 50 30.38 ± 2.53 1205.60 ± 177.23
Barrington 2016 Canada RCTs Lactoferrin 40 28.0 ± 1.7 1,087 ± 315 (1)(2)(3)
Placebo 39 28.4 ± 2.1 1,104 ± 320
Bierer 2006 United States RCTs EPO 7 26.0 ± 1.1 752 ± 150 (1)(3)
Placebo 9 26.9 ± 2.1 801 ± 103
Bin nun 2005 Israel RCTs Probiotic 72 29.8 ± 2.6 1,152 ± 262 (1)(2)(3)(4)(5)
Placebo 73 29.3 ± 4.3 1,111 ± 278
Braga 2012 Brazil RCTs Probiotic 119 29.5 ± 2.5 1194.7 ± 206.3 (1)(2)(3)(4)(5)
Placebo 112 29.2 ± 2.6 1151.4 ± 224.9
Chang 2022 China RCTs Probiotic 70 26.0 (25.0–27.0) 780.0 (689.3–915.0) (1)(2)(3)(4)(5)(6)
Placebo 50 26.0 (25.0–27.0) 815.0 (757.5–920.0)
Chaudhuri 2014 India RCTs Probiotic 56 32 ± 2 1,192 ± 341 (1)(2)(3)(5)(6)
Placebo 56 32 ± 2 1,069 ± 365
Chou 2010 China RCTs Probiotic 153 28.5 ± 2.3 1103.6 ± 232.4 (1)(2)(3)(6)
Placebo 148 28.5 ± 2.3 1097.2 ± 231.4
Costalos 2003 Greece RCTs Probiotic 51 31.1 (2.5%) 1,651 (470%) (1)(2)(5)
Placebo 36 31.8 (2.7%) 1,644 (348.7%)
Costeloe 2016 United Kingdom RCTs Probiotic 650 28.0 (26.1–29.4) 1,039 ± 312 (1)(2)(3)
Placebo 660 28.0 (26.1–29.6) 1,043 ± 317
Cui 2019 China RCTs Probiotic 45 32.85 ± 1.39 1,682 ± 109.03 (1)(2)(6)
Placebo 48 32.56 ± 1.41 1714 ± 127.11
Dallas 1998 United States RCTs Glutamine 34 24–32 500–1,250 (6)
Placebo 33 24–32 500–1,250
Dani 2002 Italy RCTs Probiotic 295 30.8 ± 2.4 1,325 ± 361 (1)(2)(4)
Placebo 290 30.7 ± 2.3 1,345 ± 384
Dekieviet 2014 Netherlands RCTs Glutamine 30 29.7 ± 1.6 1,270 ± 370 (1)
Placebo 35 29.0 ± 1.6 1,200 ± 330
Dilli 2015 Turkey RCTs Probiotic 100 28.8 ± 1.9 1,236 ± 212 (1)(2)(3)(6)
Prebiotic 100 29.0 ± 1.7 1,229 ± 246
Placebo 100 28.2 ± 2.2 1,147 ± 271
El-Ganzoury 2014 Egyp RCTs EPO 20 30.2 ± 1.8 1,310 ± 310 (1)(3)(4)(6)
Placebo 30 30.5 ± 1.5 1,360 ± 290
El-Shimi 2015 Egypt RCTs L-Arginine 25 31.84 ± 2.29 1,450 ± 260 (1)(3)(4)
Glutamine 25 31.68 ± 1.35 1,450 ± 210
Placebo 25 30.64 ± 2.34 1,310 ± 250
Fauchere 2015 Germany RCTs EPO 229 29.0 ± 1.0 1,207 ± 322 (1)(6)
Placebo 214 29.0 ± 1.0 1,215 ± 365
Fauchere 2008 Germany RCTs EPO 30 28.0 ± 2.0 1,112 ± 347 (1)(2)(3)(6)
Placebo 15 28.0 ± 2.0 1,081 ± 354
Fernandez 2012 México RCTs Probiotic 75 31.2 (26–35.4) 1,090 (580–1,495) (1)(3)(6)
Placebo 75 31 (27–36) 1,170 (540–1,492)
Fujii 2006 Japan RCTs Probiotic 11 31.3 ± 3.16 1,378 ± 365 (1)(6)
Placebo 8 31.2 ± 1.98 1,496 ± 245
Griffiths 2018 United Kingdom RCTs Lactoferrin 1,098 < 32 1125.9 ± 356.2 (1)(2)(3)(6)
Placebo 1,101 < 32 1143.3 ± 367.1
Haiden 2004 Austria RCTs EPO 21 25 (23–31) 690 (500–800) (1)(3)(6)
Placebo 19 25 (23–28) 690 (467–783)
Hays 2015 France RCTs Probiotic 145 29.0 (28.1–30.1) 1,170 (1000–1,320) (1)
Placebo 52 29.4 (27.9–30.6) 1,170 (1055–1,370)
Hoyos 1999 Colombia RCTs Probiotic 918 < 37 Not mentioned (1)(2)(3)
Placebo 935 < 37 Not mentioned
Jacobs 2013 Australia RCTs Probiotic 548 27.9 ± 2.0 1,063 ± 259 (1)(2)(3)(5)(6)
Placebo 551 27.8 ± 2.0 1,048 ± 260
Juul 2020 United States RCTs EPO 476 29.1 ± 6.2 806.4 ± 194.6 (1)(2)(3)
Placebo 470 28.8 ± 6.2 792.9 ± 182.2
Kaban 2019 Italy RCTs Probiotic 47 33 (28–34) 1,520 (1035–1800) (1)(2)(3)(6)
Placebo 47 33 (28–34) 1,605 (1060–1800)
Kanic 2015 Slovenia RCTs Probiotic 40 28.0(27.0–30.0) 1104.1 ± 233.2 (1)(2)(3)(6)
Placebo 40 29.0 (26.2–30.0) 1024.3 ± 249.9
Lacey 1996 United States RCTs Glutamine 22 26 ± 2 811 ± 175 (5)(6)
Placebo 22 26 ± 1 800 ± 155
Lin 2005 China RCTs Probiotic 180 28.5 ± 2.5 1,104 ± 242 (1)(2)(3)
Placebo 187 28.2 ± 2.5 1,071 ± 243
Lin 2008 China RCTs Probiotic 217 <34 1028.9 ± 246.0 (1)(2)(3)(5)
Placebo 217 <34 1077.3 ± 214.4
Lowe 2017 United States RCTs EPO 35 27.37 ± 1.74 500–1,250 (1)(3)
Placebo 14 27.64 ± 1.52 500–1,250
Maier 2002 Germany RCTs EPO 68 26 (25–28) 778 (660–880) (1)
Placebo 62 27 (26–28) 800 (715–885)
Manzoni 2006 Italy RCTs Probiotic 39 29.6 ± 5 1,212 ± 290 (1)(2)(3)(5)
Placebo 41 29.3 ± 4 1,174 ± 340
Manzoni 2009 Italy RCTs Lactoferrin 153 29.6 ± 2.5 1,142 ± 244 (1)(2)(3)(5)
Placebo 168 29.5 ± 3.2 1,109 ± 269
Manzoni 2014 Italy RCTs Lactoferrin 247 29.7 ± 2.5 1,158 ± 251 (1)(3)(5)
Placebo 258 29.6 ± 2.8 1,118 ± 259
Mihatsch 2010 Germany RCTs Probiotic 91 26.6 ± 1.8 856 ± 251 (1)(3)
Placebo 89 26.7 ± 1.7 871 ± 287
Modi 2010 United Kingdom RCTs Prebiotic 73 31 (29–32) 1,565 (1350–1880) (1)(2)
Placebo 81 30 (28–31) 1,515 (1247–1788)
Mohamad 2011 Malaysia RCTs Glutamine 132 Not mentioned 2,150 ± 910 (1)(2)(3)
Placebo 138 Not mentioned 2,220 ± 940
Hosseini 2019 Iran RCTs EPO 50 28.7 ± 2.6 1065.1 ± 189.4 (1)(2)(3)
Placebo 50 27.7 ± 1.5 998.1 ± 172.9
Nandhini 2015 India RCTs Probiotic 108 31.6 ± 1.4 1,430 ± 209 (1)(2)(3)
Placebo 110 31.4 ± 1.4 1,444 ± 217
Natalucci 2016 Switzerland RCTs EPO 191 29.2 ± 1.6 1,220 ± 327 (1)(2)(6)
Placebo 174 29.3 ± 1.6 1,213 ± 357
Obladen 1991 United Kingdom RCTs EPO 43 30 ± 1 1,380 ± 324 (1)(3)
Placebo 50 30 ± 1 1,295 ± 323
Ochoa 2020 United States RCTs Lactoferrin 209 30.8 ± 2.8 1,382 ± 371 (1)(2)(3)(4)(5)
Placebo 205 30.8 ± 3.2 1,378 ± 353
O’Gorman 2015 Switzerland RCTs EPO 24 30.17 ± 1.44 1,337 ± 332 (1)(2)
Placebo 34 29.5 ± 1.44 1,192 ± 10
Ohls 2013 United States RCTs EPO 32 27.8 ± 1.9 957 ± 212 (1)(3)(6)
Placebo 30 27.3 ± 1.8 933 ± 221
Ohls 2001 United States RCTs EPO 59 29 ± 2 1,130 ± 70 (1)(2)(3)(6)
Placebo 59 28 ± 2 1,118 ± 72
Ohls 2004 United States RCTs EPO 51 26.3 ± 2.0 801 ± 139 (1)(2)
Placebo 51 25.8 ± 1.7 783 ± 112
Omar 2020 Egypt RCTs EPO 36 32 (31.00–32.00) Not mentioned (1)(3)
Placebo 36 32 (30.50–32.00) Not mentioned
Oncel 2013 Turkey RCTs Probiotic 200 28.2 ± 2.4 1,071 ± 274 (1)(2)(3)(5)(6)
Placebo 200 27.9 ± 2.5 1,048 ± 298
Shannon 1995 United States RCTs EPO 77 26.8 ± 1.6 923 ± 184 (1)(2)(3)
Placebo 80 27.1 ± 1.7 925 ± 183
Demirel 2013 Turkey RCTs Probiotic 135 29.4 ± 2.3 1,164 ± 261 (1)(2)(3)(5)
Placebo 136 29.2 ± 2.5 1,131 ± 284
Dutta 2015 India RCTs Probiotic 114 30.64 ± 1.64 1286.08 ± 264.76 (1)(2)(3)
Placebo 35 30.82 ± 1.72 1252.27 ± 309.31
Güney-Varal 2017 Turkey RCTs Probiotic 70 29.7 ± 1.9 1728.5 ± 257 (1)(2)(3)(6)
Placebo 40 29.3 ± 1.7 1,228 ± 249
Singh S 2017 Austria RCTs Probiotic 37 32.6 ± 2.2 <2000 (1)
Placebo 35 32.6 ± 2.2 <2000
Patole 2014 Australia RCTs Probiotic 77 29 (26–30) 1,090 (755–1,280) (1)(2)(5)(6)
Placebo 76 28 (26–29) 1,025 (810–1,260)
Peltoniemi 2017 India RCTs EPO 21 28.3 ± 1.6 1,141 ± 230 (1)(3)
Placebo 18 28.2 ± 1.8 1,169 ± 220
Poindexter 2004 United States RCTs Glutamine 721 26.0 ± 2.1 770 ± 141 (1)(2)(3)(6)
Placebo 712 25.9 ± 1.9 768 ± 138
Polycarpou 2013 United States RCTs L-Arginine 40 29.2 (28.9–29.4) 1,168 (1095.1–1242.2) (1)(3)
Placebo 43 28.8 (28.5–29.1) 1,127 (1047.1–1207.6)
Riskin 2010 Israel RCTs Prebiotic 15 30.3 ± 2.8 1,523 ± 550 (1)(2)(3)(6)
Placebo 13 28.7 ± 2.9 1,207 ± 447
Rojas 2012 United States RCTs Probiotic 372 32(30–33) 1,530(1253–1750) (1)(3)(6)
Placebo 378 32(29–33) 1,516(1129–1750)
Rouge 2009 France RCTs Probiotic 45 28.1 ± 1.9 1,115 ± 251 (1)(2)(3)(6)
Placebo 49 28.1 ± 1.8 1,057 ± 260
Samanta 2008 India RCTs Probiotic 91 30.12 ± 1.63 1,172 ± 143 (1)(2)(3)
Placebo 95 30.14 ± 1.59 1,210 ± 143
Sari 2011 Turkey RCTs Probiotic 110 29.5 ± 2.4 1,231 ± 262 (1)(2)(3)
Placebo 111 29.7 ± 2.4 1,278 ± 282
Sari 2012 Turkey RCTs Probiotic 86 29.7 ± 2.5 1,241 ± 264 (1)(2)
Placebo 88 29.8 ± 2.3 1,278 ± 273
Serce 2013 Turkey RCTs Probiotic 104 28.7 ± 2.1 1,162 ± 216 (1)(2)(3)(6)
Placebo 104 28.8 ± 2.2 1,126 ± 232
Sevastiadou 2011 Greece RCTs Glutamine 51 30.85 ± 2.36 1,327 ± 336 (2)
Placebo 50 30.07 ± 2.47 1,283 ± 346
Shashidhar 2017 India RCTs Probiotic 48 31.2 ± 2.1 1,256 ± 185 (1)(3)(4)(5)(6)
Placebo 48 31.2 ± 2.1 1,190 ± 208
Sherman 2016 United States RCTs Lactoferrin 59 28 ± 0.85 1,152 ± 206 (1)(2)(3)(5)(6)
Placebo 60 28 ± 0.85 1,143 ± 220
Song 2016 China RCTs EPO 366 30.39 ± 1.38 1,372 ± 209 (1)(2)(3)
Placebo 377 30.40 ± 1.46 1,396 ± 239
Sowden 2022 South Africa RCTs Probiotic 100 26–36 750–1,500 (1)(4)(5)
Placebo 100 26–36 750–1,500
Stratiki 2007 Greece RCTs Probiotic 41 31(27–37) 1,500 (900–1780) (1)(2)(5)
Placebo 36 30.5(26–37) 1,500 (700–1900)
Strus 2018 Poland RCTs Probiotic 90 29.73 ± 2.26 1281.24 ± 281.18 (1)(2)(3)
Placebo 91 29.67 ± 2.32 1350.11 ± 292.18
Tanjina 2016 United Kingdom RCTs Probiotic 52 31.38 ± 0.93 1310.6 ± 110.41 (1)(5)(6)
Placebo 50 31.68 ± 0.84 1338.0 ± 97.71
Tarnow-Mordi 2020 Australia RCTs Lactoferrin 770 28.4 ± 2.4 1,068 (262) (1)(2)(3)
Placebo 771 28.4 ± 2.3 1,063 (261)
Tewari 2015 India RCTs Probiotic 61 <34 <2,500 (1)(2)(3)
Placebo 59 <34 <2,500
Thompson 2003 United Kingdom RCTs Glutamine 12 27.0 ± 1.7 862 ± 206 (5)
Placebo 16 27.8 ± 1.7 920 ± 249
Totsu 2014 Japan RCTs Probiotic 153 28.6 ± 2.9 1,016 ± 289 (1)(2)(3)(5)(6)
Placebo 150 28.5 ± 3.3 998 ± 281
Turker 2005 Turkey RCTs EPO 42 30(24–33) 1,110 (650–1,490) (1)
Placebo 51 31(24–33) 1,200 (530–1,495)
Varaporn 2014 Thailand RCTs Probiotic 31 31.0 + 1.82 1250.1 + 179.26 (1)(2)(3)(5)(6)
Placebo 29 30.59 + 1.76 1207.72 + 199.35
Vaughn 2003 United States RCTs Glutamine 314 27 ± 2 890 ± 200 (2)
Placebo 335 27 ± 2 900 ± 190
Wang 2020 China RCTs EPO 641 29.7 (28.9–30.9) 1,250 (1100–1,410) (1)
Placebo 644 30.0 (29.0–31.0) 1,300 (1100–1,450)
Wejryd 2018 Sweden RCTs Probiotic 68 25.5 ± 1.2 731 ± 129 (1)(2)(3)(5)
Placebo 66 25.5 ± 1.3 740 ± 148
Xu 2016 China RCTs Probiotic 63 33 + 0.72 1947 ± 54 (2)(5)(6)
Placebo 62 33 + 1.04 1957 ± 51
Yeo 2001 Singapore RCTs EPO 54 28.2 ± 1.9 988 ± 248 (1)(2)(3)
Placebo 54 28.3 ± 2.1 988 ± 254

Baseline characteristics of the included studies.

EPO, erythropoietin; RCT, randomized controlled trial. (1) The incidence of NEC; (2) the incidence of sepsis; (3) the incidence of overall mortality; (4) the time to beginning enteral feeds; (5) the time to full enteral feeds; (6) duration of hospitalization.

3.2 Risk of bias and quality assessment

The quality assessment of the included RCTs was conducted using the Cochrane Collaboration’s “Risk of Bias” tool. The risk of bias assessment for the included studies is presented in Table 3.

Table 3

Sequence generation Allocation concealment Blinding Completeness of data Selective reporting bias Other bias
Akin 2014 Simple envelope randomization Sealed envelope Double-blind (participant and therapist) Low risk Low risk Low risk
Al-Hosni 2012 Unclear Unclear Double-blind (participant/therapist) Low risk Low risk Low risk
Amin 2002 Unclear Unclear Double-blind (participant and therapist) Low risk Low risk Low risk
Armanian 2014 Unequal Randomization as 2:1 Unclear Unclear Low risk Low risk Low risk
Barrington 2016 Computer-generated Sealed envelope Double-blind (participant and therapist) Low risk Low risk Low risk
Bierer 2006 Permuted block method Unclear Double-blind (participant and therapist) Low risk Low risk Low risk
Bin nun 2005 Unclear Unclear Double-blind (participant and therapist) Low risk Low risk Low risk
Braga 2012 Unclear Sealed envelope Double-blind (participant and therapist) Low risk Low risk Low risk
Chang 2022 Unclear Unclear Unclear Low risk Low risk Low risk
Chaudhuri 2014 Computer-generated Unclear Double-blind (participant and therapist) Low risk Low risk Low risk
Chou 2010 Random-number table Sequence Sealed envelope Double-blind (participant and therapist) Low risk Low risk Low risk
Costalos 2003 Unclear Sealed envelope Double-blind (participant and therapist) Low risk Low risk Low risk
Costeloe 2016 Minimisation algorithm Sealed envelope Double-blind (participant and therapist) Low risk Low risk Low risk
Cui 2019 Unclear Unclear Double-blind (participant and therapist) Low risk Low risk Low risk
Dallas 1998 Unclear Unclear Double-blind (participant and therapist) Low risk Low risk Low risk
Dani 2002 Unclear Sealed envelope Double-blind (participant and therapist) Low risk Low risk Low risk
Dekieviet 2014 Unclear Unclear Unclear Low risk Low risk Low risk
Dilli 2015 Unclear Sealed envelope Double-blind (participant and therapist) Low risk Low risk Low risk
El-Ganzoury 2014 Computer-generated Sealed envelope Double-blind (participant and therapist) Low risk Low risk Low risk
El-Shimi 2015 Unclear Unclear Unclear Low risk Low risk Low risk
Fauchere 2015 Computer-generated Sealed envelope Double-blind (participant and therapist) Low risk Low risk Low risk
Fauchere 2008 Computer-generated Sealed envelope Double-blind (participant and therapist) Low risk Low risk Low risk
Fernandez 2012 Random digit table Sealed envelope Double-blind (participant and therapist) Low risk Low risk Low risk
Fujii 2006 Unclear Unclear Unclear Low risk Low risk Low risk
Griffiths 2018 Computer-generated Unclear Double-blind (participant and therapist) Low risk Low risk Low risk
Haiden2004 Unclear Sealed envelope Double-blind (participant and therapist) Low risk Low risk Low risk
Hays 2015 Unclear Unclear Triple-blind (participant and therapist and assessor) Low risk Low risk Low risk
Hoyos 1999 Unclear Unclear Unclear Low risk Low risk Low risk
Jacobs 2013 Unclear Sealed envelope Double-blind (participant and therapist) Low risk Low risk Low risk
Juul 2020 Computer-generated Sealed envelope Double-blind (participant and therapist) Low risk Low risk Low risk
Kaban 2019 Alternating Randomization technique Unclear Double-blind (participant and therapist) Low risk Low risk Low risk
Kanic 2015 Unclear Unclear Unclear Low risk Low risk Low risk
Lacey 1996 Unclear Unclear Double-blind (participant and therapist) Low risk Low risk Low risk
Lin 2005 Computer-generated Unclear Double-blind (participant and therapist) Low risk Low risk Low risk
Lin 2008 Computer-generated Unclear Double-blind (participant and therapist) Low risk Low risk Low risk
Lowe 2017 Computer-generated Unclear Double-blind (participant and therapist) Low risk Low risk Low risk
Maier 2002 Unclear Sealed envelope Double-blind (participant and therapist) Low risk Low risk Low risk
Manzoni 2006 Computer-generated Unclear Double-blind (participant and therapist) Low risk Low risk Low risk
Manzoni 2009 Computer-generated Unclear Double-blind (participant/therapist) Low risk Low risk Low risk
Manzoni 2014 Computer-generated Unclear Double-blind (participant and therapist) Low risk Low risk Low risk
Mihatsch 2010 Computer-generated Sealed envelope Double-blind (participant and therapist) Low risk Low risk Low risk
Modi 2010 Unclear Unclear Unclear Low risk Low risk Low risk
Mohamad 2011 Computer-generated Unclear Double-blind (participant and therapist) Low risk Low risk Low risk
Hosseini 2019 Unclear Unclear Double-blind (participant and therapist) Low risk Low risk Low risk
Nandhini 2015 Computer-generated Sealed envelope Double-blind (participant and therapist) Low risk Low risk Low risk
Natalucci 2016 Unclear Unclear Double-blind (participant and therapist) Low risk Low risk Low risk
Obladen 1991 Unclear Sealed envelope Double-blind (participant and therapist) Low risk Low risk Low risk
Ochoa 2020 Unclear Sealed envelope Double-blind (participant and therapist) Low risk Low risk Low risk
O’Gorman 2015 Computer-generated Sealed envelope Triple-blind (participant and therapist and assessor) Low risk Low risk Low risk
Ohls 2013 Computer-generated Sealed envelope Double-blind (participant and therapist) Low risk Low risk Low risk
Ohls 2001 Computer-generated Sealed envelope Double-blind (participant and therapist) Low risk Low risk Low risk
Ohls 2004 Unclear Unclear Unclear Low risk Low risk Low risk
Omar 2020 Unclear Unclear Double-blind (participant and therapist) Low risk Low risk Low risk
Oncel 2013 Computer-generated Sealed envelope Double-blind (participant and therapist) Low risk Low risk Low risk
Shannon 1995 Unclear Unclear Double-blind (participant and therapist) Low risk Low risk Low risk
Demirel 2013 Computer-generated Sealed envelope Double-blind (participant and therapist) Low risk Low risk Low risk
Dutta 2015 Unclear Sealed envelope Double-blind (participant and therapist) Low risk Low risk Low risk
Güney-Varal 2017 Unclear Unclear Unclear Low risk Low risk Low risk
Singh S 2017 Unclear Unclear Double-blind (participant and therapist) Low risk Low risk Low risk
Patole 2014 Unclear Unclear Double-blind (participant and therapist) Low risk Low risk Low risk
Peltoniemi 2017 Random number table Sealed envelope Double-blind (participant and therapist) Low risk Low risk Low risk
Poindexter 2004 Unclear Unclear Double-blind (participant and therapist) Low risk Low risk Low risk
Polycarpou 2013 Unclear Sealed envelope Double-blind (participant and therapist) Low risk Low risk Low risk
Riskin 2010 Unclear Sealed envelope Double-blind (participant and therapist) Low risk Low risk Low risk
Rojas 2012 Computer-generated Sealed envelope Double-blind (participant and therapist) Low risk Low risk Low risk
Rouge 2009 Unclear Unclear Double-blind (participant and therapist) Low risk Low risk Low risk
Samanta 2008 Random number table Unclear Double-blind (participant and therapist) Low risk Low risk Low risk
Sari 2011 Computer-generated Sealed envelope Double-blind (participant and therapist) Low risk Low risk Low risk
Sari 2012 Computer-generated Sealed envelope Double-blind (participant and therapist) Low risk Low risk Low risk
Serce 2013 Computer-generated Sealed envelope Double-blind (participant and therapist) Low risk Low risk Low risk
Sevastiadou 2011 Unclear Unclear Double-blind (participant and therapist) Low risk Low risk Low risk
Shashidhar 2017 Computer-generated Sealed envelope Double-blind (participant and therapist) Low risk Low risk Low risk
Sherman 2016 Unclear Unclear Double-blind (participant and therapist) Low risk Low risk Low risk
Song 2016 Computer-generated Sealed envelope Double-blind (participant and therapist) Low risk Low risk Low risk
Sowden 2022 Unclear Unclear Double-blind (participant and therapist) Low risk Low risk Low risk
Stratiki 2007 Unclear Unclear Double-blind (participant and therapist) Low risk Low risk Low risk
Strus 2018 Unclear Unclear Double-blind (participant and therapist) Low risk Low risk Low risk
Tanjina 2016 Unclear Unclear Double-blind (participant and therapist) Low risk Low risk Low risk
Tarnow-Mordi 2020 Computer-generated Sealed envelope Double-blind (participant and therapist) Low risk Low risk Low risk
Tewari 2015 Unclear Unclear Double-blind (participant and therapist) Low risk Low risk Low risk
Thompson 2003 Unclear Unclear Double-blind (participant and therapist) Low risk Low risk Low risk
Totsu 2014 Computer-generated Unclear Double-blind (participant and therapist) Low risk Low risk Low risk
Turker 2005 Unclear Unclear Unclear Low risk Low risk Low risk
Varaporn 2014 Unclear Unclear Double-blind (participant and therapist) Low risk Low risk Low risk
Vaughn 2003 Unclear Sealed envelope Double-blind (participant and therapist) Low risk Low risk Low risk
Wang 2020 Unclear Unclear Double-blind (participant and therapist) Low risk Low risk Low risk
Wejryd 2018 Computer-generated Sealed envelope Double-blind (participant and therapist) Low risk Low risk Low risk
Xu 2016 Unclear Unclear Double-blind (participant and therapist) Low risk Low risk Low risk
Yeo 2001 Unclear Unclear No-blind Low risk Low risk Low risk

Risk of bias of the included randomized controlled trials.

3.3 Evidence network

This study encompassed 6 drugs (7 interventions), including lactoferrin, probiotics, prebiotics, glutamine, arginine, erythropoietin and placebo. Figure 2 represents the evidence network, where the lines denote direct comparisons between two directly related interventions. Interventions lacking direct connections are compared indirectly through the NMA. The width of the lines reflects the number of trials, while the size of the nodes corresponds to the total sample size across multiple treatments.

Figure 2

Figure 2

Network analysis of eligible comparison for (A) the incidence of NEC, (B) the incidence of sepsis, (C) the incidence of overall mortality, (D) time to beginning enteral feeds, (E) time to full enteral feeds and (F) duration of hospitalization. The size of each node represents the number of participants, while the thickness of the line represents the number of studies directly comparing the two interventions.

3.4 Inconsistency test

Figure 3 displays an inconsistency plot designed to assess heterogeneity among studies within the closed loops of the NMA. There were 5 closed loops for the primary outcomes including the incidence of NEC, NEC-associated sepsis and overall mortality, with IF ranging from 0.47 to 6.52. Most of these closed loops had 95% CIs that contained 0, and only one closed loops of probiotics-prebiotics-placebo had 95% CIs approaching 0. Overall, these results suggest that the data exhibited consistency.

Figure 3

Figure 3

Inconsistency plot of eligible comparison for (A) the incidence of NEC, (B) the incidence of sepsis and (C) the incidence of overall mortality.

3.5 NMA results

3.5.1 Primary outcomes

3.5.1.1 The incidence of NEC

A total of 83 RCTs with 25,359 neonates reported the incidence of NEC after treatment, involving interventions of probiotics, prebiotics, glutamine, lactoferrin, EPO, arginine, and placebo. The results of the NMA revealed the following findings regarding the incidence of NEC: arginine therapy was associated with lower incidence of NEC compared lactoferrin (RR = 0.39, 95%CI: 0.18, 0.87), EPO (RR = 2.25, 95%CI: 1.07, 4.75), glutamine (RR = 3.08, 95%CI: 1.34, 7.10) and placebo (RR = 3.12, 95%CI: 1.55, 6.31). Probiotics therapy was associated with a lower incidence of NEC compared glutamine (RR = 1.78, 95%CI: 1.08, 2.94) and placebo (RR = 1.81, 95%CI: 1.45, 2.25). Other comparisons did not yield statistically significant differences (Figure 4A).

Figure 4

Figure 4

Forest plots for (A) the incidence of NEC, (B) the incidence of sepsis, (C) the incidence of overall mortality, (D) time to beginning enteral feeds, (E) time to full enteral feeds and (F) duration of hospitalization.

A ranking graph illustrating the distribution of probabilities for NEC is presented in Figure 5A. The SUCRA rankings for the incidence of NEC were as follows: arginine (3.2%) < probiotics (22.2%) < prebiotics (45.8%) < EPO (48.5%) < lactoferrin (61.7%) < glutamine (81.6%) < placebo (87.1%), which suggests that arginine is associated with the lowest probability of developing NEC while placebo has the lowest effect. Therefore, the efficacy in reducing the incidence of NEC was ranked from best to worst as follows: arginine, probiotics, prebiotics, EPO, lactoferrin, glutamine, and placebo.

Figure 5

Figure 5

Surface under the cumulative ranking (SUCRA) for (A) the incidence of NEC, (B) the incidence of sepsis, (C) the incidence of overall mortality, (D) time to beginning enteral feeds, (E) time to full enteral feeds and (F) duration of hospitalization.

3.5.1.2 The incidence of NEC-associated sepsis

A total of 62 RCTs involving 20,994 neonates reported the incidence of post-treatment sepsis. The results of the NMA revealed that lactoferrin (RR = 1.55, 95% CI: 1.10, 2.19) and probiotics (RR = 1.23, 95% CI: 1.06, 1.44) had a higher effect on NEC-associated sepsis compared to placebo. Other comparisons did not yield statistically significant differences (Figure 4B).

A ranking graph illustrating the distribution of probabilities for NEC-associated sepsis is presented in Figure 5B. The SUCRA rankings for the incidence of NEC-associated sepsis were as follows: lactoferrin (18.7%) < prebiotics (31.3%) < EPO (42.9%) < probiotics (46.7%) < arginine (50.5%) < glutamine (77%) < placebo (83%), suggesting that lactoferrin was associated with the lowest probability of developing NEC-associated sepsis while placebo had the lowest effect. Therefore, the efficacy in reducing the incidence of NEC-associated sepsis was ranked from best to worst as follows: lactoferrin, prebiotics, EPO, probiotics, arginine, glutamine, and placebo.

3.5.1.3 The incidence of overall mortality

Sixty-two RCTs involving 20,438 neonates reported the incidence of overall mortality. The results of the NMA revealed that probiotics exhibited a lower incidence of overall mortality compared to placebo (RR = 1.46, 95%CI: 1.16, 1.83). Other comparisons did not yield statistically significant differences (Figure 4C).

A ranking graph illustrating the distribution of probabilities for overall mortality is presented in Figure 5C. The SUCRA rankings for the incidence of overall mortality were as follows: prebiotics (11.1%) < arginine (28.5%) < probiotics (35.3%) < EPO (45.9%) < lactoferrin (69.4%) < glutamine (74.9%) < placebo (84.8%), suggesting that prebiotics was associated with the lowest overall mortality while placebo had the lowest effect. Therefore, the efficacy in reducing the incidence of overall mortality was ranked from best to worst as follows: prebiotics, arginine, probiotics, EPO, lactoferrin, glutamine, and placebo.

3.5.2 Secondary outcomes

3.5.2.1 Time to beginning enteral feeds

Only 11 RCTs involving 2,144 neonates reported the time to beginning enteral feeds. The results of the NMA revealed the following findings: glutamine demonstrated a longer time compared to probiotics (WMD = 8.01, 95%CI: 1.95, 32.88), arginine (WMD = 4.39, 95%CI: 1.08, 17.87) and placebo (WMD = 0.15, 95%CI: 0.04, 0.57). Other comparisons did not yield statistically significant differences (Figure 4D).

A ranking graph illustrating the distribution of probabilities for the time to beginning enteral feeds is presented in Figure 5D. Based on the SUCRA, probiotics had the lowest SUCRA rank, indicating the lowest probability of the time to beginning enteral feeds, while glutamine had the highest probability. The SUCRA rankings for time to beginning enteral feeds were as follows: probiotics (20.1%) < placebo (33.9%) < lactoferrin (38.6%) < prebiotics (48.8%) < arginine (52.3%) < EPO (59.8%) < glutamine (96.4%). Therefore, the efficacy in shortening the time to beginning enteral feeds was ranked from best to worst as follows: probiotics, placebo, lactoferrin, prebiotics, arginine, EPO, and glutamine.

3.5.2.2 Time to full enteral feeds

A total of 27 RCTs with 5,916 neonates reported the time to full enteral feeds, involving five interventions including glutamine, prebiotics, probiotics, lactoferrin, and placebo. The NMA results revealed the following findings: probiotics demonstrated a shorter time to full enteral feeds compared to placebo (WMD = 5.95, 95%CI: 2.67, 13.26). Other comparisons did not yield statistically significant differences (Figure 4E).

A ranking graph illustrating the distribution of probabilities for the time to full enteral feeds is presented in Figure 5E. Based on the SUCRA, glutamine had the lowest SUCRA rank, indicating the lowest probability of the time to full enteral feeds, while placebo had the highest probability. The SUCRA rankings for time to full enteral feeds were as follows: glutamine (25.9%) < prebiotics (34.6%) < probiotics (47.8%) < lactoferrin (47.9%) < placebo (93.8%). Therefore, the efficacy in shortening the time to full enteral feeds was ranked from best to worst as follows: glutamine, prebiotics, probiotics, lactoferrin, and placebo.

3.5.2.3 Duration of hospitalization

A total of 34 RCTs with 9,642 neonates reported duration of hospitalization, involving six interventions, including lactoferrin, probiotics, prebiotics, glutamine, EPO, and placebo. The NMA results revealed the following: probiotics demonstrated a shorter duration of hospitalization compared to placebo (WMD = 25.6, 95%CI: 2.81, 233.54). Other comparisons did not yield statistically significant differences (Figure 4F).

A ranking graph illustrating the distribution of probabilities for duration of hospitalization is presented in Figure 5F. Based on the SUCRA, prebiotics had the lowest SUCRA rank, indicating the lowest probability of duration of hospitalization, while placebo had the highest probability. The SUCRA rankings for duration of hospitalization were as follows: prebiotics (13.8%) < probiotics (38.1%) < glutamine (43.5%) < EPO (51.1%) < lactoferrin (73%) < placebo (80.5%). Therefore, the efficacy in shortening duration of hospitalization was ranked from best to worst as follows: prebiotics, probiotics, glutamine, EPO, lactoferrin, and placebo.

3.6 Publication bias

Based on the outcomes observed for the incidence of NEC, NEC-associated sepsis, overall mortality, time to beginning enteral feeds, time to full enteral feeds and duration of hospitalization, NMA showed that the corrected funnel plots were generated to assess publication bias and potential small sample effects. The analysis revealed that most data points were well-distributed within the funnel plot, displaying relative symmetry on both sides. Additionally, the regression line closely paralleled the X-axis, indicating minimal likelihood of publication bias or small sample effects (Figure 6).

Figure 6

Figure 6

Funnel plots of (A) the incidence of NEC, (B) the incidence of sepsis, (C) the incidence of overall mortality, (D) time to beginning enteral feeds, (E) time to full enteral feeds and (F) duration of hospitalization.

4 Discussion

NEC continues to be one of the most severe acute GI afflictions in preterm and low-birth-weight infants (30). However, its precise etiology and pathogenesis are still not fully understood (31). Key factors implicated in NEC include intestinal mucosal barrier dysfunction, ischemia–reperfusion injury, inflammatory responses, and an imbalance in gut microbiota (32). Without effective treatments for NEC, research has shifted toward prevention strategies. Early initiation of breastfeeding has shown to be beneficial, particularly in preterm and low birth weight infants (5, 6, 33). However, the susceptibility to NEC is paradoxically increased (3335) due to dysfunctional suckling and swallowing, GI reflux, and impaired motor coordination (3638). As a result, parenteral nutrition is commonly initiated in these infants. The search for alternative NEC prevention methods has led to the discovery that probiotics, prebiotics, arginine, lactoferrin, EPO, and glutamine have significant roles in the primary prevention of NEC (17, 19, 20). With advancing insights into the pathogenesis of NEC, new avenues for prevention and treatment are continually being explored.

This study integrates data from 89 RCTs on six interventions (including probiotics, prebiotics, arginine, lactoferrin, EPO, and glutamine), utilizing NMA to evaluate their impact on NEC incidence, NEC-associated sepsis and mortality, and to rank their probabilities of efficacy. NMA indicated the following ranking from most to least effective in decreasing the incidence of NEC in preterm infants: arginine, probiotics, prebiotics, erythropoietin, lactoferrin, glutamine, placebo; for the reduction of NEC-associated sepsis events: lactoferrin, prebiotics, erythropoietin, probiotics, arginine, glutamine, placebo; and for the reduction of overall mortality: prebiotics, arginine, probiotics, erythropoietin, lactoferrin, glutamine, placebo. The ranking for time to beginning enteral feeds was: probiotics, placebo, lactoferrin, prebiotics, arginine, erythropoietin, glutamine; for time to full enteral feeds: glutamine, prebiotics, probiotics, lactoferrin, placebo; and for hospital stay duration: prebiotics, probiotics, glutamine, erythropoietin, lactoferrin, placebo. A comprehensive analysis of these six outcomes suggests an overall clinical efficacy ranking from most to least effective for the aforementioned drugs as follows: arginine, probiotics, prebiotics, lactoferrin, erythropoietin, glutamine, and placebo.

Intestinal microcirculatory perfusion is predominantly regulated by nitric oxide (NO), a vasodilator synthesized via the activity of endothelial nitric oxide synthase (eNOS) (7). Upon entry of harmful bacteria into the circulation, expression levels of eNOS are suppressed. Decreased plasma NO levels can lead to significant vasoconstriction, disrupts intestinal perfusion and result in hypoxia, a hallmark of necrosis seen in NEC. To boost eNOS activity, Moreira et al. (39) incorporated arginine into their research, an amino acid precursor to NO that is crucial for preventing tissue injury (40). A deficiency in endogenous arginine synthesis can restrict NO production and impair vasodilation in the postprandial intestinal circulation. Chen et al. (41) discovered that arginine supplementation increases blood flow within the intestinal microvasculature and can prevent NEC, whereas arginine antagonists may intensify the condition. The findings of the present study further indicate that arginine significantly reduces the incidence of NEC in premature infants, which aligns with the recent findings by Wang et al. (42). Moreover, arginine demonstrates a substantial advantage in decreasing the incidence of sepsis and overall mortality.

Compared to placebo, lactoferrin showed a statistically significant difference in efficacy in reducing the incidence of NEC and NEC-associated sepsis. Acccording to probability ranking, lactoferrin is the most effective intervention in decreasing the incidence of NEC-associated sepsis, outperforming other measures. These findings largely align with prior meta-analytic conclusions (43, 44). The broad-spectrum antimicrobial effects of lactoferrin are likely due to its multiple mechanisms of action, including cell membrane disruption, iron sequestration, immune modulation, and direct antimicrobial activity, which collectively inhibit the growth of bacteria, fungi, and viruses (45). This contributes to reducing the incidence of advanced NEC stages, specifically stages II and III NEC (44). However, there is a discrepancy with the findings of Gao’s study (46), potentially due to limited study inclusion and a small sample size.

Prebiotics showed superior efficacy in reducing overall mortality and hospital stay of NEC patients. Prebiotics naturally present in breast milk, comprising over 200 varieties of human milk oligosaccharides (HMOs) (47). These prebiotics promote the proliferation of beneficial microbes such as Bifidobacteria and Lactobacilli. Their life-saving potential is likely due to the prevention of pathogen colonization and the unchecked growth of opportunistic pathogens (48). Furthermore, prebiotics enhance gut motility and permeability in preterm infants, thus improving intestinal epithelium integrity. The synergistic effects of pathogen inhibition and the prevention of their adherence to the epithelial surface may bolster the resistance of preterm infants to endogenous infections (49, 50). This study also corroborates that probiotics expedite the initiation of postnatal enteral feeding. Aligning with the findings by Athalye-Jape et al. (51), this may be attributed to the promotion of GI maturity and motility through the extension of intestinal transit time, acceleration of gastric emptying, and augmentation of mesenteric arterial blood flow post-probiotic administration.

The present study has some limitations. First, only English-language literature was included. Secondly, the interpretability of findings is restricted due to inadequate details on randomization methods and allocation concealment in many trials. Thirdly, an economic analysis was not performed.

Despite these limitations, the key strengths of this paper are: (1) an expanded evaluation of interventional drugs based on prior research, offering a broader comparison of clinical efficacies for preventing NEC in preterm infants, with results reflecting the most comprehensive current evidence; (2) inclusion of 89 RCTs, addressing the previous meta-analyses limitations of the limited study scope and sample size, thus providing a more robust evidence base.

5 Conclusion

Existing literature highlights arginine as the most efficacious pharmacological agent in preventing NEC in preterm infants. It has been shown to effectively lower the rates of NEC, septicemia, and mortality, warranting its recommendation as the first-line clinical intervention. Following this, probiotics are recommended as a second option.

Statements

Data availability statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

Author contributions

JC: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing. XC: Data curation, Methodology, Resources, Supervision, Writing – original draft, Writing – review & editing. XH: Investigation, Resources, Supervision, Visualization, Writing – review & editing. JL: Resources, Supervision, Writing – review & editing. QY: Resources, Supervision, Writing – review & editing.

Funding

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This work was support by the Neijiang Science and Technology Plan Project (grant number 2024NJJCYJZYY003).

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Summary

Keywords

preterm infants, necrotizing enterocolitis, drugs, network meta-analysis, randomized controlled trials

Citation

Chen J, Chen X, Huang X, Liu J and Yu Q (2024) Comparative efficacy of different single drugs to prevent necrotizing enterocolitis in preterm infants: an update systematic review and network meta-analysis. Front. Nutr. 11:1452338. doi: 10.3389/fnut.2024.1452338

Received

20 June 2024

Accepted

27 August 2024

Published

09 September 2024

Volume

11 - 2024

Edited by

Teleky Bernadette-Emoke, University of Agricultural Sciences and Veterinary Medicine of Cluj-Napoca, Romania

Reviewed by

Mesfin Abebe, Dilla University, Ethiopia

Nikolai Kolba, Cornell University, United States

Xiaohan Hu, Children’s Hospital of Soochow University, China

Updates

Copyright

*Correspondence: Jing Chen,

†These authors have contributed equally to this work and share first authorship

Disclaimer

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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