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

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

AuthorCountryStudy designGroupNOGestational age (week)Birth weight (g)Outcome
Akin 2014TurkeyRCTsLactoferrin2229.5 ± 1.61,290 ± 346.7(1)(2)(3)
Placebo2330.3 ± 2.51,307 ± 262.1
Al-Hosni 2012United StatesRCTsProbiotic5025.7 ± 1.4778 ± 138(1)(2)(3)
Placebo5125.7 ± 1.4779 ± 126
Amin 2002CanadaRCTsL-arginine7527.4 ± 0.3952 ± 25(1)(2)
Placebo7727.6 ± 0.2955 ± 20
Armanian 2014IranRCTsPrebiotic2530.48 ± 2.311262.80 ± 213.35(1)(2)(3)(4)(5)(6)
Placebo5030.38 ± 2.531205.60 ± 177.23
Barrington 2016CanadaRCTsLactoferrin4028.0 ± 1.71,087 ± 315(1)(2)(3)
Placebo3928.4 ± 2.11,104 ± 320
Bierer 2006United StatesRCTsEPO726.0 ± 1.1752 ± 150(1)(3)
Placebo926.9 ± 2.1801 ± 103
Bin nun 2005IsraelRCTsProbiotic7229.8 ± 2.61,152 ± 262(1)(2)(3)(4)(5)
Placebo7329.3 ± 4.31,111 ± 278
Braga 2012BrazilRCTsProbiotic11929.5 ± 2.51194.7 ± 206.3(1)(2)(3)(4)(5)
Placebo11229.2 ± 2.61151.4 ± 224.9
Chang 2022ChinaRCTsProbiotic7026.0 (25.0–27.0)780.0 (689.3–915.0)(1)(2)(3)(4)(5)(6)
Placebo5026.0 (25.0–27.0)815.0 (757.5–920.0)
Chaudhuri 2014IndiaRCTsProbiotic5632 ± 21,192 ± 341(1)(2)(3)(5)(6)
Placebo5632 ± 21,069 ± 365
Chou 2010ChinaRCTsProbiotic15328.5 ± 2.31103.6 ± 232.4(1)(2)(3)(6)
Placebo14828.5 ± 2.31097.2 ± 231.4
Costalos 2003GreeceRCTsProbiotic5131.1 (2.5%)1,651 (470%)(1)(2)(5)
Placebo3631.8 (2.7%)1,644 (348.7%)
Costeloe 2016United KingdomRCTsProbiotic65028.0 (26.1–29.4)1,039 ± 312(1)(2)(3)
Placebo66028.0 (26.1–29.6)1,043 ± 317
Cui 2019ChinaRCTsProbiotic4532.85 ± 1.391,682 ± 109.03(1)(2)(6)
Placebo4832.56 ± 1.411714 ± 127.11
Dallas 1998United StatesRCTsGlutamine3424–32500–1,250(6)
Placebo3324–32500–1,250
Dani 2002ItalyRCTsProbiotic29530.8 ± 2.41,325 ± 361(1)(2)(4)
Placebo29030.7 ± 2.31,345 ± 384
Dekieviet 2014NetherlandsRCTsGlutamine3029.7 ± 1.61,270 ± 370(1)
Placebo3529.0 ± 1.61,200 ± 330
Dilli 2015TurkeyRCTsProbiotic10028.8 ± 1.91,236 ± 212(1)(2)(3)(6)
Prebiotic10029.0 ± 1.71,229 ± 246
Placebo10028.2 ± 2.21,147 ± 271
El-Ganzoury 2014EgypRCTsEPO2030.2 ± 1.81,310 ± 310(1)(3)(4)(6)
Placebo3030.5 ± 1.51,360 ± 290
El-Shimi 2015EgyptRCTsL-Arginine2531.84 ± 2.291,450 ± 260(1)(3)(4)
Glutamine2531.68 ± 1.351,450 ± 210
Placebo2530.64 ± 2.341,310 ± 250
Fauchere 2015GermanyRCTsEPO22929.0 ± 1.01,207 ± 322(1)(6)
Placebo21429.0 ± 1.01,215 ± 365
Fauchere 2008GermanyRCTsEPO3028.0 ± 2.01,112 ± 347(1)(2)(3)(6)
Placebo1528.0 ± 2.01,081 ± 354
Fernandez 2012MéxicoRCTsProbiotic7531.2 (26–35.4)1,090 (580–1,495)(1)(3)(6)
Placebo7531 (27–36)1,170 (540–1,492)
Fujii 2006JapanRCTsProbiotic1131.3 ± 3.161,378 ± 365(1)(6)
Placebo831.2 ± 1.981,496 ± 245
Griffiths 2018United KingdomRCTsLactoferrin1,098< 321125.9 ± 356.2(1)(2)(3)(6)
Placebo1,101< 321143.3 ± 367.1
Haiden 2004AustriaRCTsEPO2125 (23–31)690 (500–800)(1)(3)(6)
Placebo1925 (23–28)690 (467–783)
Hays 2015FranceRCTsProbiotic14529.0 (28.1–30.1)1,170 (1000–1,320)(1)
Placebo5229.4 (27.9–30.6)1,170 (1055–1,370)
Hoyos 1999ColombiaRCTsProbiotic918< 37Not mentioned(1)(2)(3)
Placebo935< 37Not mentioned
Jacobs 2013AustraliaRCTsProbiotic54827.9 ± 2.01,063 ± 259(1)(2)(3)(5)(6)
Placebo55127.8 ± 2.01,048 ± 260
Juul 2020United StatesRCTsEPO47629.1 ± 6.2806.4 ± 194.6(1)(2)(3)
Placebo47028.8 ± 6.2792.9 ± 182.2
Kaban 2019ItalyRCTsProbiotic4733 (28–34)1,520 (1035–1800)(1)(2)(3)(6)
Placebo4733 (28–34)1,605 (1060–1800)
Kanic 2015SloveniaRCTsProbiotic4028.0(27.0–30.0)1104.1 ± 233.2(1)(2)(3)(6)
Placebo4029.0 (26.2–30.0)1024.3 ± 249.9
Lacey 1996United StatesRCTsGlutamine2226 ± 2811 ± 175(5)(6)
Placebo2226 ± 1800 ± 155
Lin 2005ChinaRCTsProbiotic18028.5 ± 2.51,104 ± 242(1)(2)(3)
Placebo18728.2 ± 2.51,071 ± 243
Lin 2008ChinaRCTsProbiotic217<341028.9 ± 246.0(1)(2)(3)(5)
Placebo217<341077.3 ± 214.4
Lowe 2017United StatesRCTsEPO3527.37 ± 1.74500–1,250(1)(3)
Placebo1427.64 ± 1.52500–1,250
Maier 2002GermanyRCTsEPO6826 (25–28)778 (660–880)(1)
Placebo6227 (26–28)800 (715–885)
Manzoni 2006ItalyRCTsProbiotic3929.6 ± 51,212 ± 290(1)(2)(3)(5)
Placebo4129.3 ± 41,174 ± 340
Manzoni 2009ItalyRCTsLactoferrin15329.6 ± 2.51,142 ± 244(1)(2)(3)(5)
Placebo16829.5 ± 3.21,109 ± 269
Manzoni 2014ItalyRCTsLactoferrin24729.7 ± 2.51,158 ± 251(1)(3)(5)
Placebo25829.6 ± 2.81,118 ± 259
Mihatsch 2010GermanyRCTsProbiotic9126.6 ± 1.8856 ± 251(1)(3)
Placebo8926.7 ± 1.7871 ± 287
Modi 2010United KingdomRCTsPrebiotic7331 (29–32)1,565 (1350–1880)(1)(2)
Placebo8130 (28–31)1,515 (1247–1788)
Mohamad 2011MalaysiaRCTsGlutamine132Not mentioned2,150 ± 910(1)(2)(3)
Placebo138Not mentioned2,220 ± 940
Hosseini 2019IranRCTsEPO5028.7 ± 2.61065.1 ± 189.4(1)(2)(3)
Placebo5027.7 ± 1.5998.1 ± 172.9
Nandhini 2015IndiaRCTsProbiotic10831.6 ± 1.41,430 ± 209(1)(2)(3)
Placebo11031.4 ± 1.41,444 ± 217
Natalucci 2016SwitzerlandRCTsEPO19129.2 ± 1.61,220 ± 327(1)(2)(6)
Placebo17429.3 ± 1.61,213 ± 357
Obladen 1991United KingdomRCTsEPO4330 ± 11,380 ± 324(1)(3)
Placebo5030 ± 11,295 ± 323
Ochoa 2020United StatesRCTsLactoferrin20930.8 ± 2.81,382 ± 371(1)(2)(3)(4)(5)
Placebo20530.8 ± 3.21,378 ± 353
O’Gorman 2015SwitzerlandRCTsEPO2430.17 ± 1.441,337 ± 332(1)(2)
Placebo3429.5 ± 1.441,192 ± 10
Ohls 2013United StatesRCTsEPO3227.8 ± 1.9957 ± 212(1)(3)(6)
Placebo3027.3 ± 1.8933 ± 221
Ohls 2001United StatesRCTsEPO5929 ± 21,130 ± 70(1)(2)(3)(6)
Placebo5928 ± 21,118 ± 72
Ohls 2004United StatesRCTsEPO5126.3 ± 2.0801 ± 139(1)(2)
Placebo5125.8 ± 1.7783 ± 112
Omar 2020EgyptRCTsEPO3632 (31.00–32.00)Not mentioned(1)(3)
Placebo3632 (30.50–32.00)Not mentioned
Oncel 2013TurkeyRCTsProbiotic20028.2 ± 2.41,071 ± 274(1)(2)(3)(5)(6)
Placebo20027.9 ± 2.51,048 ± 298
Shannon 1995United StatesRCTsEPO7726.8 ± 1.6923 ± 184(1)(2)(3)
Placebo8027.1 ± 1.7925 ± 183
Demirel 2013TurkeyRCTsProbiotic13529.4 ± 2.31,164 ± 261(1)(2)(3)(5)
Placebo13629.2 ± 2.51,131 ± 284
Dutta 2015IndiaRCTsProbiotic11430.64 ± 1.641286.08 ± 264.76(1)(2)(3)
Placebo3530.82 ± 1.721252.27 ± 309.31
Güney-Varal 2017TurkeyRCTsProbiotic7029.7 ± 1.91728.5 ± 257(1)(2)(3)(6)
Placebo4029.3 ± 1.71,228 ± 249
Singh S 2017AustriaRCTsProbiotic3732.6 ± 2.2<2000(1)
Placebo3532.6 ± 2.2<2000
Patole 2014AustraliaRCTsProbiotic7729 (26–30)1,090 (755–1,280)(1)(2)(5)(6)
Placebo7628 (26–29)1,025 (810–1,260)
Peltoniemi 2017IndiaRCTsEPO2128.3 ± 1.61,141 ± 230(1)(3)
Placebo1828.2 ± 1.81,169 ± 220
Poindexter 2004United StatesRCTsGlutamine72126.0 ± 2.1770 ± 141(1)(2)(3)(6)
Placebo71225.9 ± 1.9768 ± 138
Polycarpou 2013United StatesRCTsL-Arginine4029.2 (28.9–29.4)1,168 (1095.1–1242.2)(1)(3)
Placebo4328.8 (28.5–29.1)1,127 (1047.1–1207.6)
Riskin 2010IsraelRCTsPrebiotic1530.3 ± 2.81,523 ± 550(1)(2)(3)(6)
Placebo1328.7 ± 2.91,207 ± 447
Rojas 2012United StatesRCTsProbiotic37232(30–33)1,530(1253–1750)(1)(3)(6)
Placebo37832(29–33)1,516(1129–1750)
Rouge 2009FranceRCTsProbiotic4528.1 ± 1.91,115 ± 251(1)(2)(3)(6)
Placebo4928.1 ± 1.81,057 ± 260
Samanta 2008IndiaRCTsProbiotic9130.12 ± 1.631,172 ± 143(1)(2)(3)
Placebo9530.14 ± 1.591,210 ± 143
Sari 2011TurkeyRCTsProbiotic11029.5 ± 2.41,231 ± 262(1)(2)(3)
Placebo11129.7 ± 2.41,278 ± 282
Sari 2012TurkeyRCTsProbiotic8629.7 ± 2.51,241 ± 264(1)(2)
Placebo8829.8 ± 2.31,278 ± 273
Serce 2013TurkeyRCTsProbiotic10428.7 ± 2.11,162 ± 216(1)(2)(3)(6)
Placebo10428.8 ± 2.21,126 ± 232
Sevastiadou 2011GreeceRCTsGlutamine5130.85 ± 2.361,327 ± 336(2)
Placebo5030.07 ± 2.471,283 ± 346
Shashidhar 2017IndiaRCTsProbiotic4831.2 ± 2.11,256 ± 185(1)(3)(4)(5)(6)
Placebo4831.2 ± 2.11,190 ± 208
Sherman 2016United StatesRCTsLactoferrin5928 ± 0.851,152 ± 206(1)(2)(3)(5)(6)
Placebo6028 ± 0.851,143 ± 220
Song 2016ChinaRCTsEPO36630.39 ± 1.381,372 ± 209(1)(2)(3)
Placebo37730.40 ± 1.461,396 ± 239
Sowden 2022South AfricaRCTsProbiotic10026–36750–1,500(1)(4)(5)
Placebo10026–36750–1,500
Stratiki 2007GreeceRCTsProbiotic4131(27–37)1,500 (900–1780)(1)(2)(5)
Placebo3630.5(26–37)1,500 (700–1900)
Strus 2018PolandRCTsProbiotic9029.73 ± 2.261281.24 ± 281.18(1)(2)(3)
Placebo9129.67 ± 2.321350.11 ± 292.18
Tanjina 2016United KingdomRCTsProbiotic5231.38 ± 0.931310.6 ± 110.41(1)(5)(6)
Placebo5031.68 ± 0.841338.0 ± 97.71
Tarnow-Mordi 2020AustraliaRCTsLactoferrin77028.4 ± 2.41,068 (262)(1)(2)(3)
Placebo77128.4 ± 2.31,063 (261)
Tewari 2015IndiaRCTsProbiotic61<34<2,500(1)(2)(3)
Placebo59<34<2,500
Thompson 2003United KingdomRCTsGlutamine1227.0 ± 1.7862 ± 206(5)
Placebo1627.8 ± 1.7920 ± 249
Totsu 2014JapanRCTsProbiotic15328.6 ± 2.91,016 ± 289(1)(2)(3)(5)(6)
Placebo15028.5 ± 3.3998 ± 281
Turker 2005TurkeyRCTsEPO4230(24–33)1,110 (650–1,490)(1)
Placebo5131(24–33)1,200 (530–1,495)
Varaporn 2014ThailandRCTsProbiotic3131.0 + 1.821250.1 + 179.26(1)(2)(3)(5)(6)
Placebo2930.59 + 1.761207.72 + 199.35
Vaughn 2003United StatesRCTsGlutamine31427 ± 2890 ± 200(2)
Placebo33527 ± 2900 ± 190
Wang 2020ChinaRCTsEPO64129.7 (28.9–30.9)1,250 (1100–1,410)(1)
Placebo64430.0 (29.0–31.0)1,300 (1100–1,450)
Wejryd 2018SwedenRCTsProbiotic6825.5 ± 1.2731 ± 129(1)(2)(3)(5)
Placebo6625.5 ± 1.3740 ± 148
Xu 2016ChinaRCTsProbiotic6333 + 0.721947 ± 54(2)(5)(6)
Placebo6233 + 1.041957 ± 51
Yeo 2001SingaporeRCTsEPO5428.2 ± 1.9988 ± 248(1)(2)(3)
Placebo5428.3 ± 2.1988 ± 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 generationAllocation concealmentBlindingCompleteness of dataSelective reporting biasOther bias
Akin 2014Simple envelope randomizationSealed envelopeDouble-blind (participant and therapist)Low riskLow riskLow risk
Al-Hosni 2012UnclearUnclearDouble-blind (participant/therapist)Low riskLow riskLow risk
Amin 2002UnclearUnclearDouble-blind (participant and therapist)Low riskLow riskLow risk
Armanian 2014Unequal Randomization as 2:1UnclearUnclearLow riskLow riskLow risk
Barrington 2016Computer-generatedSealed envelopeDouble-blind (participant and therapist)Low riskLow riskLow risk
Bierer 2006Permuted block methodUnclearDouble-blind (participant and therapist)Low riskLow riskLow risk
Bin nun 2005UnclearUnclearDouble-blind (participant and therapist)Low riskLow riskLow risk
Braga 2012UnclearSealed envelopeDouble-blind (participant and therapist)Low riskLow riskLow risk
Chang 2022UnclearUnclearUnclearLow riskLow riskLow risk
Chaudhuri 2014Computer-generatedUnclearDouble-blind (participant and therapist)Low riskLow riskLow risk
Chou 2010Random-number table SequenceSealed envelopeDouble-blind (participant and therapist)Low riskLow riskLow risk
Costalos 2003UnclearSealed envelopeDouble-blind (participant and therapist)Low riskLow riskLow risk
Costeloe 2016Minimisation algorithmSealed envelopeDouble-blind (participant and therapist)Low riskLow riskLow risk
Cui 2019UnclearUnclearDouble-blind (participant and therapist)Low riskLow riskLow risk
Dallas 1998UnclearUnclearDouble-blind (participant and therapist)Low riskLow riskLow risk
Dani 2002UnclearSealed envelopeDouble-blind (participant and therapist)Low riskLow riskLow risk
Dekieviet 2014UnclearUnclearUnclearLow riskLow riskLow risk
Dilli 2015UnclearSealed envelopeDouble-blind (participant and therapist)Low riskLow riskLow risk
El-Ganzoury 2014Computer-generatedSealed envelopeDouble-blind (participant and therapist)Low riskLow riskLow risk
El-Shimi 2015UnclearUnclearUnclearLow riskLow riskLow risk
Fauchere 2015Computer-generatedSealed envelopeDouble-blind (participant and therapist)Low riskLow riskLow risk
Fauchere 2008Computer-generatedSealed envelopeDouble-blind (participant and therapist)Low riskLow riskLow risk
Fernandez 2012Random digit tableSealed envelopeDouble-blind (participant and therapist)Low riskLow riskLow risk
Fujii 2006UnclearUnclearUnclearLow riskLow riskLow risk
Griffiths 2018Computer-generatedUnclearDouble-blind (participant and therapist)Low riskLow riskLow risk
Haiden2004UnclearSealed envelopeDouble-blind (participant and therapist)Low riskLow riskLow risk
Hays 2015UnclearUnclearTriple-blind (participant and therapist and assessor)Low riskLow riskLow risk
Hoyos 1999UnclearUnclearUnclearLow riskLow riskLow risk
Jacobs 2013UnclearSealed envelopeDouble-blind (participant and therapist)Low riskLow riskLow risk
Juul 2020Computer-generatedSealed envelopeDouble-blind (participant and therapist)Low riskLow riskLow risk
Kaban 2019Alternating Randomization techniqueUnclearDouble-blind (participant and therapist)Low riskLow riskLow risk
Kanic 2015UnclearUnclearUnclearLow riskLow riskLow risk
Lacey 1996UnclearUnclearDouble-blind (participant and therapist)Low riskLow riskLow risk
Lin 2005Computer-generatedUnclearDouble-blind (participant and therapist)Low riskLow riskLow risk
Lin 2008Computer-generatedUnclearDouble-blind (participant and therapist)Low riskLow riskLow risk
Lowe 2017Computer-generatedUnclearDouble-blind (participant and therapist)Low riskLow riskLow risk
Maier 2002UnclearSealed envelopeDouble-blind (participant and therapist)Low riskLow riskLow risk
Manzoni 2006Computer-generatedUnclearDouble-blind (participant and therapist)Low riskLow riskLow risk
Manzoni 2009Computer-generatedUnclearDouble-blind (participant/therapist)Low riskLow riskLow risk
Manzoni 2014Computer-generatedUnclearDouble-blind (participant and therapist)Low riskLow riskLow risk
Mihatsch 2010Computer-generatedSealed envelopeDouble-blind (participant and therapist)Low riskLow riskLow risk
Modi 2010UnclearUnclearUnclearLow riskLow riskLow risk
Mohamad 2011Computer-generatedUnclearDouble-blind (participant and therapist)Low riskLow riskLow risk
Hosseini 2019UnclearUnclearDouble-blind (participant and therapist)Low riskLow riskLow risk
Nandhini 2015Computer-generatedSealed envelopeDouble-blind (participant and therapist)Low riskLow riskLow risk
Natalucci 2016UnclearUnclearDouble-blind (participant and therapist)Low riskLow riskLow risk
Obladen 1991UnclearSealed envelopeDouble-blind (participant and therapist)Low riskLow riskLow risk
Ochoa 2020UnclearSealed envelopeDouble-blind (participant and therapist)Low riskLow riskLow risk
O’Gorman 2015Computer-generatedSealed envelopeTriple-blind (participant and therapist and assessor)Low riskLow riskLow risk
Ohls 2013Computer-generatedSealed envelopeDouble-blind (participant and therapist)Low riskLow riskLow risk
Ohls 2001Computer-generatedSealed envelopeDouble-blind (participant and therapist)Low riskLow riskLow risk
Ohls 2004UnclearUnclearUnclearLow riskLow riskLow risk
Omar 2020UnclearUnclearDouble-blind (participant and therapist)Low riskLow riskLow risk
Oncel 2013Computer-generatedSealed envelopeDouble-blind (participant and therapist)Low riskLow riskLow risk
Shannon 1995UnclearUnclearDouble-blind (participant and therapist)Low riskLow riskLow risk
Demirel 2013Computer-generatedSealed envelopeDouble-blind (participant and therapist)Low riskLow riskLow risk
Dutta 2015UnclearSealed envelopeDouble-blind (participant and therapist)Low riskLow riskLow risk
Güney-Varal 2017UnclearUnclearUnclearLow riskLow riskLow risk
Singh S 2017UnclearUnclearDouble-blind (participant and therapist)Low riskLow riskLow risk
Patole 2014UnclearUnclearDouble-blind (participant and therapist)Low riskLow riskLow risk
Peltoniemi 2017Random number tableSealed envelopeDouble-blind (participant and therapist)Low riskLow riskLow risk
Poindexter 2004UnclearUnclearDouble-blind (participant and therapist)Low riskLow riskLow risk
Polycarpou 2013UnclearSealed envelopeDouble-blind (participant and therapist)Low riskLow riskLow risk
Riskin 2010UnclearSealed envelopeDouble-blind (participant and therapist)Low riskLow riskLow risk
Rojas 2012Computer-generatedSealed envelopeDouble-blind (participant and therapist)Low riskLow riskLow risk
Rouge 2009UnclearUnclearDouble-blind (participant and therapist)Low riskLow riskLow risk
Samanta 2008Random number tableUnclearDouble-blind (participant and therapist)Low riskLow riskLow risk
Sari 2011Computer-generatedSealed envelopeDouble-blind (participant and therapist)Low riskLow riskLow risk
Sari 2012Computer-generatedSealed envelopeDouble-blind (participant and therapist)Low riskLow riskLow risk
Serce 2013Computer-generatedSealed envelopeDouble-blind (participant and therapist)Low riskLow riskLow risk
Sevastiadou 2011UnclearUnclearDouble-blind (participant and therapist)Low riskLow riskLow risk
Shashidhar 2017Computer-generatedSealed envelopeDouble-blind (participant and therapist)Low riskLow riskLow risk
Sherman 2016UnclearUnclearDouble-blind (participant and therapist)Low riskLow riskLow risk
Song 2016Computer-generatedSealed envelopeDouble-blind (participant and therapist)Low riskLow riskLow risk
Sowden 2022UnclearUnclearDouble-blind (participant and therapist)Low riskLow riskLow risk
Stratiki 2007UnclearUnclearDouble-blind (participant and therapist)Low riskLow riskLow risk
Strus 2018UnclearUnclearDouble-blind (participant and therapist)Low riskLow riskLow risk
Tanjina 2016UnclearUnclearDouble-blind (participant and therapist)Low riskLow riskLow risk
Tarnow-Mordi 2020Computer-generatedSealed envelopeDouble-blind (participant and therapist)Low riskLow riskLow risk
Tewari 2015UnclearUnclearDouble-blind (participant and therapist)Low riskLow riskLow risk
Thompson 2003UnclearUnclearDouble-blind (participant and therapist)Low riskLow riskLow risk
Totsu 2014Computer-generatedUnclearDouble-blind (participant and therapist)Low riskLow riskLow risk
Turker 2005UnclearUnclearUnclearLow riskLow riskLow risk
Varaporn 2014UnclearUnclearDouble-blind (participant and therapist)Low riskLow riskLow risk
Vaughn 2003UnclearSealed envelopeDouble-blind (participant and therapist)Low riskLow riskLow risk
Wang 2020UnclearUnclearDouble-blind (participant and therapist)Low riskLow riskLow risk
Wejryd 2018Computer-generatedSealed envelopeDouble-blind (participant and therapist)Low riskLow riskLow risk
Xu 2016UnclearUnclearDouble-blind (participant and therapist)Low riskLow riskLow risk
Yeo 2001UnclearUnclearNo-blindLow riskLow riskLow 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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