The therapeutic sc"enario of metastatic renal cell cancer (mRCC) has noticeably increased, ranging from the most studied molecular target therapies to those most recently introduced, up to immune checkpoint inhibitors (ICIs). The most recent clinical trials with an ICI-based combination of molecular targeted agents and ICI show how, by restoring an efficient immune response against cancer cells and by establishing an immunological memory, it is possible to obtain not only a better radiological response but also a longer progression-free and overall survival. However, the role of tyrosine kinase inhibitors (TKIs) remains of fundamental importance, especially in patients who, for clinical characteristics, tumor burden and comorbidity, could have greater benefit from the use of TKIs in monotherapy rather than in combination with other therapies. However, to use these novel options in the best possible way, knowledge is required not only of the data from the large clinical trials but also of the biological mechanisms, molecular pathways, immunological mechanisms, and methodological issues related to both new response criteria and endpoints. In this complex scenario, we review the latest results of the latest clinical trials and provide guidance for overcoming the barriers to decision-making to offer a practical approach to the management of mRCC in daily clinical practice. Moreover, based on recent literature, we discuss the most innovative combination strategies that would allow us to achieve the best clinical therapeutic results. Renal cancer is the 10th most common cancer in Italy, with approximately 13,400 new cases per year  remains poor, especially in patients with unfavorable prognostic factors . The twoThe therapeutic scenario of mRCC has undergone incredible enrichment in recent years, ranging from the most studied tyrosine kinase inhibitor (TKI)-targeted therapies  and anti-mTOR) to those most recently introduced  up to immunotherapy (IO) . Literature data on new therapeutic indications with cabozantinib in both the first and second lines , 6, nivoIn the era of precision medicine, in which knowledge of the molecular and genomic aspects of renal cancer has become ever wider, how can we think that criteria based on obvious clinical considerations  and hematochemical parameters are sufficient to determine a therapeutic choice? To make the best use of new drugs and associations and to propose new therapeutic sequences, better knowledge is required not only of the data derived from the large clinical trials but also of the basic biology, the complexity of involved molecular pathways, the immunology of tumours, and methodological problems related to both new response criteria and new endpoints. In this complex scenario, this review aims to provide a practical approach to the management of advanced renal cancer, framing the new results in daily clinical practice and providing points for reflections to overcome decision-making barriers based on physician therapeutic choice.α), which plays a key role in the cellular response to hypoxic conditions. It stimulates the transcription of genes involved in promoting angiogenesis and invasive growth. In renal cancer cells, this complex does not function; therefore, HIF-1α accumulates in cells and activates a cascade of other genes that encode factors that induce hypoxia, including VEGF or those involved in alternative pathways to VEGF, such as fibroblast growth factor receptor (FGFR), platelet-derived growth factor receptors (PDGFRs), AXL, and c-MET, all of which are involved in angiogenesis, tumor growth, and survival  is a VHL target. VHL loss-of-function mutations usually result in an increased abundance and nuclear localization of ZHX2. Loss of ZHX2 inhibits signaling through the transcription factor NF- for RCC .e.g. the VHL gene in the clear cell tumor) that paves the way for tumorigenesis, and from the trunk, different subclonal mutations branch out, which contribute to tumor growth and progression. Data from the TRACERx renal study have identified secondary mutations and chromosomal changes involved in tumor evolution  has identified 19 genes involved in addition to  and p53 . The coner cells Figure 1In addition to proper genetic damage, we must consider the variations induced by the environment (epigenetics), alterations in receptor expression, and all the complexity that revolves around the tumor microenvironment. TP53 and KRAS mutations , which represent the targets of known ICIs, such as anti-CTLA4, anti-PD-1, or anti-PD-L1, are found within the tumor microenvironment. In addition to playing a key role in the carcinogenesis process, some parameters such as the expression of PD-L1 have been associated with a worse prognosis  as well +FOXP3+) and express high levels of PD-L1, thus demonstrating the conditions associated with a worse prognosis , RET, ROS1, TYRO3, MER, KIT (stem cell factor), TRKB, Fms-like tyrosine kinase-3 (FLT3), and TIE-2 . Based ovs two), and duration of first treatment with VEGFR-TKI (≤6 months vs >6 months). Dose reductions were more frequent with cabozantinib than with everolimus, but no statistically significant difference in terms of discontinuation of severe adverse events was reported , were randomized 1:1 to receive cabozantinib n = 330) or everolimus (n = 328), including those who may have previously been treated with other therapies, including cytokines and antibodies directed against VEGF, the PD-1 receptor, or other ligands. Additionally, patients with treated brain metastases were included. The primary endpoint of the study was progression-free survival (PFS). Secondary endpoints were objective response rate (ORR) and overall survival (OS). Most patients were males (75%), with a median age of 62 years. Seventy-one percent of patients had previously been treated with only one VEGFR-TKI. In 41% of patients, sunitinib was the single VEGFR-TKI previously received. According to the MSCKK criteria for the prognostic risk category, in 46% of patients, the prognosis was favorable; in 42%, it was intermediate (one risk factor); and in 13%, it was poor (two or three risk factors). In 54% of patients, three or more organs, including the lungs (63%), lymph nodes (62%), liver (29%), and bones (22%), had metastatic disease. The median duration of treatment was 7.6 months (range 0.3–20.5) for patients who received cabozantinib and 4.4 months (range 0.2–18.9) for patients who received everolimus. A statistically significant improvement has been demonstrated in PFS for cabozantinib compared to everolimus . In a subsequent interim analysis, a statistically significant improvement was also demonstrated in terms of OS . Comparable OS results were observed with a follow-up analysis (descriptive) at 430 events. Exploratory analyses of PFS and OS in the intent-to-treat population also showed consistent results in favor of cabozantinib compared to everolimus in different subgroups defined by age (<65 years compared to ≥65 years), sex, risk group, ECOG status (0 compared to 1), time from diagnosis to randomisation (<1 year compared to ≥1 year), tumor expression of MET (high compared to low compared to unknown), bone metastasis, visceral metastasis, number of VEGFR-TKIs previously received  were randomized 1:1 to receive cabozantinib (n = 79) or sunitinib with a schedule of 4 weeks on/2 weeks off (n = 78). The patients were stratified according to the IMDC risk category (81% intermediate and 19% poor) and the presence or absence of bone metastases. Approximately 75% of patients underwent nephrectomy before the start of treatment. The primary endpoint was the PFS, and the secondary endpoints were ORR and OS. Most patients were males (78%) with a median age of 62 years. Most patients (87%) had an ECOG performance status of 0 or 1; 13% had an ECOG performance status of 2. Thirty-six percent of the patients had bone metastases. The study has reached the primary endpoint of statistically significant improvement of the PFS for cabozantinib compared to sunitinib . Patients showed a favorable effect with cabozantinib compared to sunitinib irrespective of MET status (positive or negative); however, cabozantinib demonstrated greater activity in patients with positive MET status than that in patients with negative MET status [HR = 0.32 (0.16 and 0.63) vs 0.67 (0.37 and 1.23)]. In addition, compared to the treatment with sunitinib, treatment with cabozantinib has been associated with a trend of longer OS   and were generally found during the first 8 weeks of treatment. In the METEOR study, dosing reductions and dosing interruptions of 59.8 and 70%, respectively, occurred in relation to an adverse event caused by cabozantinib. In CABOSUN, where patients were naïve to treatment, the percentages of reduction and treatment interruption were quite similar . Therefore, it does not seem to be a condition of drug toxicity. However, hypertension has been observed more frequently in the population of naïve patients (67%) than in patients included in the METEOR trial who had been previously treated with anti-VEGF targets (37%).vs 5%, p < 0.001, HR for OS = 0.73; 95% confidence interval (CI) = 0.57–0.93). However, no significant advantages in terms of PFS have been reported.Nivolumab was the first anti-PD-1 ICI approved for the treatment of mRCC, first as monotherapy in patients previously exposed to a VEGFR-TKI and then in combination with ipilimumab as the first-line treatment in patients with intermediate- and poor-risk prognosis. According to data from the Phase III Checkmate 025, patients who progressed during or after 1–2 previous anti-angiogenic regimens were eligible for treatment with nivolumab monotherapy . This stvs 27%, p < 0.001), regardless of the expression level of PD-L1, although in the PD-L1 >1% group, the advantage was even more significant . The PFS was not significantly different between the two groups . In addition, in the 249 patients at favorable risk, nivolumab plus ipilimumab was detrimental in terms of OS compared to sunitinib (HR = 1.13 [0.64–1.99] p = 0.6710). In terms of tolerability, the combination of ipilimumab and nivolumab was burdened with a higher toxicity than sunitinib   of efficacy by the number of IMDC risk factors was completed. ORR with nivolumab plus ipilimumab was consistent across zero to six IMDC risk factors, whereas with sunitinib, it decreased with an increasing number of risk factors. The benefits of nivolumab plus ipilimumab over sunitinib in terms of ORR (40–44% vs 16–38%), OS (HR = 0.50–0.72), and PFS (HR = 0.44–0.86) were consistently observed in subgroups with one, two, three, or four to six IMDC risk factors. These results demonstrate the benefit of first-line nivolumab plus ipilimumab over sunitinib across all intermediate- and poor-risk groups, regardless of the number of IMDC risk factors (Nivolumab in combination with ipilimumab proved to be superior to sunitinib as the first-line therapy in the Phase III study Checkmate 214 . The stuoxicity) . Howeveroxicity) . Moreove factors .Thanks to the data reported, the combination of nivolumab and ipilimumab was approved by ESMO guidelines in intermediate- and poor-risk prognostic subgroups of mRCC.post-hoc analysis of nivolumab plus ipilimumab or sunitinib in IMDC intermediate/poor-risk patients with previously untreated mRCC with sarcomatoid features showed an ORR of 56.7%  in the combination arm against 19.2% (9.6–32.5) of standard treatment and a rate of complete response (CR) of 18.3% in the experimental group, whereas no CR was observed in the sunitinib arm  only a subset of patients achieves objective responses, ii) some patients have a delayed response, and iii) a significant number of patients do not benefit even clinically. In detail, although the so-called ‘combo’ IO is particularly active as the upfront treatment in patients with intermediate/poor prognosis, it cannot be a universal choice for all patients, but only for those patients ‘fit’ for a more intensive combined treatment. Moreover, the ipilimumab–nivolumab combination was less effective than sunitinib in patients over 75 years of age, who represent most of those we met in clinical practice. Therefore, IO is an important strategy both as first- and second-line treatment in patients with mRCC, but TKI agents remain the central focus of mRCC treatment in all therapeutic lines. Several hypotheses have been formulated regarding the lack of efficacy of ICIs in all patients, and among these, tumor heterogeneity and the dynamism of the tumor microenvironment typical of renal cancer cells seem to be the main conditions , 42.The upfront combination of VEGF-targeting agents with ICIs is emerging as a therapeutic alternative that could overcome the limitations of IO alone as well as target both the cascade of angiogenesis and the tumor microenvironment Figure 1vs 34.9 months in the sunitinib arm, and the results (HR = 0.93) had not yet crossed the significance boundary  population, the median OS was 33.6 months in the combination arm boundary . A pre-sboundary .Other promising combinations always used as first-line treatment are axitinib plus avelumab and axitinib plus pembrolizumab, tested in the Javelin Renal 101  and the vs 8.0 months (95% CI = 6.7e9.8), HR = 0.69 [95% CI = 0.574–0.825]; p < 0.0001); moreover, the combination prolonged PFS2 compared with sunitinib. However, OS data (primary endpoint of both studies) are still immature  and ORR  in favor of the combined treatment arm, disregarding the status of PD-L1 and the prognostic risk category  with sunitinib; HR = 0.68 (95% CI = 0.55–0.85), p = 0.0003], median PFS , and ORR (60% in the combo arm vs 40% in the sunitinib group). Although the trial was not designed to observe differences between risk categories, it should be noted that the benefit in terms of OS was particularly evident in the population at intermediate and unfavorable risk [pembrolizumab plus axitinib vs sunitinib: HR = 0.63 (95% CI = 0.50–0.81)], while it was not significant in the favorable risk group . Moreover, in terms of toxicity, no significant news emerged with the continued follow-up of patients in the study. The most frequent treatment-related grade 3 or higher adverse events (10% of patients in both groups) were hypertension [95 (22%) of 429 patients in the pembrolizumab group plus axitinib vs 84 (20%) of 425 patients in the sunitinib group], increased alanine aminotransferase levels [54 (13%) vs 11 (3%)], and diarrhea [46 (11%) vs 23 (5%)] (The results of the extended follow-up of the randomized phase III study KEYNOTE-426 (median follow-up 30.6 months) confirmed the benefit for the experimental arm, which was proven statistically significant in terms of median OS . In patients treated with the combination cabozantinib and nivolumab, the median PFS, the primary endpoint of the study, is doubled compared to patients who received only sunitinib: 16.6 months compared to 8.3 months . In addition, cabozantinib in combination with nivolumab showed a higher ORR (56 vs 27%) and 8% of patients compared to 5% who achieved a complete response. Moreover, the combination treatment was associated with a longer response duration compared to sunitinib, with a median duration of 20.2 months compared to 11.5 months. In addition, patients treated with the combination showed a lower rate of discontinuation of treatment than those treated with sunitinib (44.4 vs 71.3%) and a significantly lower rate of discontinuation for disease progression than sunitinib (27.8 vs 48.1%). All these key efficacy results are consistent in pre-specified subgroups and all risk categories according to the IMDC and PD-L1 expression  for first-line treatment of patients with mRCC , and shovs TKI–TKI–mTORi is. However, the next future question will be much more complex since there are no comparative studies, clear prognostic factors, or predictive markers, thus making a weighted choice between the various options available in the first- and second-line very difficult.For a decade, it has been wondered what the best sequence treatment between TKI–mTORi–TKI The new treatment strategies range from molecular targeted agents such as cabozantinib, able to overcome some anti-angiogenic mechanisms of resistance, through ICIs, such as nivolumab, as a single agent, up to the combinations of ICIs (nivolumab + ipilimumab), or between ICIs with VEGF-targeting agents .The paradigm of first-line treatment in advanced RCC, firmly occupied for more than 10 years by monotherapy with anti-angiogenic TKIs, such as sunitinib or pazopanib, has changed, and combinations of ICIs, either with each other or with TKIs, have shown efficacy compared to monotherapy with TKIs. In light of the results of recent combinations, except for comorbidity and clinical contraindications, in the first-line, the therapeutic proposal is to administer all prognostic classes the combination TKI/IO (axitinib plus pembrolizumab/nivolumab plus cabozantinib) or IO/IO (nivolumab plus ipilimumab), and considering the combination IO/IO for patients with sarcomatoid components, whereas all other cases remain valid for TKI monotherapy, in particular, cabozantinib in the intermediate- and high-risk subgroups unfit for combo treatment, and pazopanib or sunitinib in the good risk unfit for combo .Whether the objective is to achieve a complete response (CR) as well as a long survival (or possible cure), ipilimumab plus nivolumab or nivolumab plus cabozantinib would be the treatment of choice. In fact, the CR rates in CheckMate 214, CheckMate9ER, Keynote426, and Javeline Renal 101 were 9, 8, 5.8, and 3.8%, respectively. In contrast, we should also consider that a higher rate of progressive disease (PD) was observed as the best response to treatment in the CheckMate 214 trial, while the lowest was observed in CheckMate9ER. The toxicity profile is a further discriminant in the choice of combination treatment. In fact, for the IO–IO combination, the major toxicities are limited to the induction phase with ipilimumab, while for the combination of an ICI and a VEGFR-TKI, safety issues tend to persist over time due to the prolonged administration of both agents.As the field stands now, the immuno-target combination could represent a particularly valid opportunity, especially in patients with a ‘cold’ phenotype, whose tumor is characterized by poor immune infiltration and are considered less likely to respond to ICI-based treatment alone.Given the lack of head-to-head comparative studies, both experience and common sense must guide the choice of a physician according to the following considerations: i) patient characteristics, comorbidities, drug interactions with concurrent therapy, occupation, preferences of patient, and side effects that can affect the quality of life; ii) neoplasia features, its histology, if it has a representative sarcomatoid components, the genetic structure, the burden of cancer disease, and the location of the metastases and their related symptoms; iii) balancing the risk and benefit of treatment itself: for safety, we should consider that the trade-off between efficacy and safety that a first-line patient is willing to accept is usually unbalanced in favor of efficacy; iv) biological aggressiveness of the tumor: in the case of an aggressive disease, the combo IO/TKI seems a very reasonable choice to control disease growth while waiting for the tail effect of IO; otherwise, one could head for the long-term benefit of the IO/IO combo, as well as for complete response, trying to spare the additional toxicity derived from the continuous use of the VEGFR-TKI.Moreover, a recent meta-analysis network on the choice of the first-line showed that cabozantinib is the best molecular targeted agent for the advantage in terms of PFS in patients at intermediate/poor risk compared to sunitinib, with a 91% chance of giving the best benefit in PFS . Therefo33 and recent retrospective real-world studies , but this advantage was not observed when PD-L1 was less than 1% . Conversely, a better ORR and OS for IO over an anti-vascular agent was reported regardless of tumor PD-L1 expression level  . In tumo+ lymphocytes, and M1 macrophages may be associated with a better prognosis, whereas infiltration of regulatory T cells, such as Tregs and M2 macrophages, have a poorer outcome  and the increased expression of immune markers (e.g. BACH2 and CCL3), respectively  and circulating tumor cells (CTCs) are peripherally detectable tumor-derived materials. These markers could detect primary and metastatic sites non-invasively and evaluate the response to therapy –97. Varivs 2.25; p = 0.0002)  . Additio 0.0002) –103, andThere are three possible treatment strategies in the therapeutic landscape of mRCC: angiogenic inhibitors at one end, IO at the other, and combinations of the two classes in the middle. The challenge, however, is identifying the subset of patients who could benefit from one therapeutic class alone to avoid the unnecessary toxicity of combination approaches.PBRM1 mutation was frequently associated)  ; Clusterociated) ; and Cluociated) .PBRM1 mutations, seemed to benefit more from sunitinib, but not from atezolizumab either alone or in combination with bevacizumab. The combination treatment improved clinical benefits compared with sunitinib in T-effector high tumours. Tumors with T-effector high and lower myeloid inflammation-associated gene expression benefited from atezolizumab monotherapy. Instead, in T-effector high tumors, a concomitant high myeloid inflammation predicted a worse response to IO alone. Myeloid inflammation is associated with high expression of IL-6, prostaglandins, and the CXCL8 family of chemokines, which suppress the anti-tumor immunity. The improved clinical outcome associated with atezolizumab + bevacizumab compared with atezolizumab monotherapy in this subgroup suggests that the addition of bevacizumab to atezolizumab may overcome innate inflammation-mediated resistance in these tumors  with therapeutic response. Highly angiogenic tumors, which coincided with tumors exhibiting e tumors .Based on the analysis of the angiogenic profile in comparison with the immunological profile of the study IMmotion151, it is possible to define subgroups of tumors that benefit from different treatment strategies. Given the new associations, it would be interesting to evaluate these aspects in other combinations of IO/TKI and see if, for example, the addition of TKI modifies the immunogenicity of these tumors.Considering the continuously evolving scenario in the treatment of patients with mRCC, the future goal will be to better characterize renal neoplasia in all its complexity, from the trunk to the last of its branches. However, to outline the most appropriate treatment path for each patient, we cannot deny that only clinical criteria are very likely to understand the needs of patients. Given the significant improvement in therapeutic options, prospective studies are needed that would elucidate: what will be the most effective therapeutic algorithm and how patients will be selected to hit more targets; will it be more effective to use therapeutic agents in sequence or by focusing completely on the first therapeutic line; will it be more effective to use a combination strategy from the beginning of mRCC treatment? Further studies are required to answer these questions.All authors have read and agreed to the published version of the manuscript. All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.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.


Prematurity coupled with the necessary clinical management of preterm (PT) infants introduces multiple factors that can interfere with microbial colonization. This study aimed to review the perinatal, physiological, pharmacological, dietary, and environmental factors associated with gut microbiota of PT infants. A total of 587 articles were retrieved from a search of multiple databases. Sixty studies were included in the review after removing duplicates and articles that did not meet the inclusion criteria. Review of this literature revealed that evidence converged on the effect of postnatal age, mode of delivery, use of antibiotics, and consumption of human milk in the composition of gut microbiota of PT infants. Less evidence was found for associations with race, sex, use of different fortifiers, macronutrients, and other medications. Future studies with rich metadata are needed to further explore the impact of the PT exposome on the development of the microbiota in this high-risk population. Studies in full-term infants have shown that the characteristics of gut microbial communities are determined by multiple factors, including postnatal age, mode of delivery, diet, antibiotic exposure,6 and 64% in the USA.7 These rates are higher than the prevalence of C-section delivery in full-term infants, which is about 21%.8 Given their prematurity and compromised health status, PT infants can remain hospitalized in the neonatal intensive care unit (NICU) for an extended period of time after birth. The total length of stay varies depending on the growth and development of each infant. Infants born at an earlier gestational age (GA) and with lower birth weight spend more time in the NICU.9 As part of their medical care, PT infants receive many medications that can influence the gut microbiota, particularly antibiotics. Another critical factor in the treatment of these infants is how they are fed. The goal of the dietary treatments is to optimize the infant’s growth by providing adequate calories, macronutrients, and micronutrients via parenteral or enteral routes.10 To achieve the nutritional goals, PT infants can be fed different types of milk and fortifiers during the course of their hospitalization.11 Taken together, these factors can profoundly influence the establishment of the gut microbiota of infants born preterm.Preterm infants, born less than 37 weeks of gestation, experience many physiological, medical, dietary, and environmental challenges that can detrimentally affect their microbial colonization. The rates of PT birth by cesarean section (C-section) are around 31% worldwide,13 and is implicated in cognitive development.14 If this homeostasis is altered by external factors, a dysbiosis in the gut ecosystem can occur, with a greater presence and abundance of pathogenic bacteria.15 In PT infants, the gut microbial composition is often characterized as dysbiotic,16 with slower acquisition and an overall lower prevalence of beneficial bacteria.17 This dysbiosis appears to be associated with a higher risk of developing serious complications including sepsis, and necrotizing enterocolitis (NEC),19 which can have detrimental long-term effects on the infant’s health, including disruption in neurodevelopment. Previous systematic reviews have investigated how various factors influence PT infants microbiome, including antibiotic use,20 enteral feeding,21 and the hospital environment.22 However, these factors do not work in isolation, and no previous systematic review has attempted to capture the full complexity of factors shaping PT infants microbiome. Thus, the goal of this review was to review the literature available regarding the impact of perinatal, physiological, pharmacological, dietary, and environmental factors on the composition of the gut microbiota of PT infants. By holistically examining the multifactorial influences on colonization of PT infant’s gut, gaps in the literature will be identified, which will highlight the opportunities for novel interventions aiming to optimize the establishment of these bacterial communities of infants born preterm.The way the microbiome develops in early life is critically important, as key mutualistic relationships exist between the host, bacterial communities, and their metabolites. Additionally, the microbiome shapes immune development,23This systematic review was registered in the PROSPERO database (CRD42020131964) and was conducted according to the guidelines of the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA).A systematic search was performed in four databases  from May to July of 2019. The search terms included: “preterm infant”, “premature infant”, “extremely premature infant” “gut microbiome”, “gut microbiota”, “gastrointestinal microbiome”, “fecal microbiota”, “maternal health”, “gestational age”, “mode of delivery”, “C-section”, “cesarean section”, “immaturity”, “neonatal intensive care unit”, “NICU”, “hospital environment”, “hospitals”, “antibiotics”, “anti-bacterial agents”, “medication”, “parenteral nutrition”, “enteral nutrition”, “breastfeeding”, “human milk”, “mother’s milk”, “donor human milk”, “preterm formula”, “infant formula”, “probiotic”, “probiotics”, “prebiotic”, “prebiotics”, and “milk fortifier”.24 After the literature search, all obtained articles were independently assessed by the two authors (MAL and AMD) to determine those to be included in the review. In the case of disagreements, a third author (SMD) resolved the discrepancies.To be eligible, studies needed to be focused on the gut microbiota of PT infants, conducted in human subjects, and be a cross-sectional, longitudinal, or s clinical trial study. Articles were excluded if they were not in English, no full-text was available, and were published before 2009, when advanced sequencing technologies were not widely used.The information extracted from each study included: author, year of publication, geographic location of the study sample, study design, sample size, length of study, intervention or exposure, (if applicable), intervention characteristics (if applicable), control group (if applicable), gut microbiota assessment method, 16S rRNA variable region (if applicable), sequencing platform (if applicable), alpha diversity, beta diversity, taxonomy, other gut microbiota related outcomes, and clinical outcomes.25 This tool assesses potential research biases in five domains: bias arising from the randomization process, bias due to deviations from intended interventions, bias due to missing outcome data, bias in measurement of the outcomes, and bias in selection of the reported result. From these domains, an overall risk of bias was assigned to each study. A study was considered as “low risk of bias” if it showed low risk an all five domains, “some concerns” if it raised concerns in at least one of the domains, and “high risk of bias” if a study was high risk of bias in at least one of the domains, or scored “some concerns” in more than one domain.25 Cross-sectional or longitudinal studies were assessed using the Newcastle-Ottawa Scale (NOS) for observational studies.26 This tool measures four domains, including participant selection, comparability, exposure, and outcome. The scoring is based on number of stars, cross-sectional studies could receive up to six stars, and longitudinal studies could score a maximum of nine stars.26 All the selected articles were assessed by MAL and AMD.Eligible clinical trials were assessed using the Cochrane Collaboration Tool for assessing risk of bias (RoB2).in vitro (n = 4), scope of the study (n = 14), year of publication (n = 3), and no abstract availability (n = 5). In total, 71 articles underwent full-text review. In this step, 11 articles were removed due to text not being available in English (n = 2), no availability of full text (n = 2), or scope of the study (n = 7). As shown in , studies performed in animal models or shown in , a totalSupplementary Table 1, included bacterial DNA sequencing, bacterial culture, denaturing, and temperature gradient gel electrophoresis (DGGE and TGGE), terminal restriction fragment length polymorphism (T-RFLP), pulsed-field gel electrophoresis (PFGE), fluorescent in situ hybridization (FISH), matrix assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF) and microarrays. Most studies used next-generation sequencing (NGS) technologies targeting the V3-V4, V4, and V3-V5 regions of the 16S rRNA bacterial gene. The platforms used included Ion Torrent, Roche 454 GS FLX Titanium, and Illumina technologies.Characteristics of the 60 articles are presented in . The aveSupplementary Table 2, came from the randomization process,27 deviation from the intended intervention,28–30 and the measurement of the outcomes.31 One study,31 was found to have high risk of bias, and thus it was not included in the description of the results. Evaluation of cohort and case-control studies using NOS are shown in Supplementary Table 3 and Supplementary Table 4, respectively. Among the cohort studies, 39.4% had a score of nine stars  scored “Some concerns” for the risk of bias. The primary source of bias, summarized in 32–34 chorioamnionitis, which is the bacterial infection of the membranes of the placenta and amniotic fluid,35 prenatal antibiotics,32 and antenatal steroids35 on the gut microbiota composition of PT infants. Infants from mothers who had PROM and/or chorioamnionitis  during pregnancy had lower alpha diversity over time compared to those infants whose mothers did not develop these complications.32 However, this association was significantly confounded by the use of antibiotics. Cong et al. found that PROM explained ~2% of the variation of the beta diversity from gut microbiota of PT infants.33 Chernikova et al. described that, regardless of the use of antibiotics, PT infants exposed to prolonged PROM had higher abundances of Staphylococcus and Streptococcus across time; these infants also showed faster increase in the abundance of Enterobacter, and lower colonization with Clostridium over time.32 In contrast, Zwittink et al. found no association between the gut microbiota composition of PT infants and the exposure to PROM.34 Infants exposed to chorioamnionitis during gestation, had greater abundances of Serratia, Parabacteroides, and Bradyrhizobium independent of the use of antibiotics.32 It is important to mention that Bradyrhizobium has been described as a common contaminant from NGS techniques which can be detected in samples with low microbial biomass.36 Another observational study found that the relative abundance of Gammaproteobacteria was positively associated with antenatal steroids.35 This same study demonstrated that PT infants showed two different gut bacterial community patterns described as clusters. Cluster 1 with low abundances of Gammaproteobacteria and Cluster 2 with high abundances of Gammaproteobacteria.35 When PT infants from Cluster 2 were exposed to chorioamnionitis  during gestation, the abundances of Gammaproteobacteria were lower, whereas PT infants from Cluster 2 exposed to antenatal steroids had higher abundances of Gammaproteobacteria.35Four observational studies, shown in , reporte32 However, a large number of studies reported no associations between mode of delivery and alpha diversity.37–42 Similarly, most of the studies found no differences in beta diversity by mode of delivery.43–45 Only two observational studies reported that mode of delivery explained 1.93%37 to 12%41 of the variation of beta diversity of the fecal microbiota of PT infants. It is important to note that results from Butcher et al. came from PT infants exclusively fed mother’s own milk (MOM).37 A cohort study analyzed the fecal microbiota composition of PT infants during early and late feeding stages, representing 1 to 2 weeks and 2 to 4 weeks after the introduction of enteral feeding, respectively. The authors found that mode of delivery was significantly associated with beta diversity (Unweighted UniFrac distances) only during the late feeding time points.46A total of 21 studies reported associations between mode of delivery and the characteristics of the gut microbiota of PT infants, shown in . One lon47 Ho et al. reported that the abundance of Firmicutes was positively associated with birth via C-section.35 At class level, this same study reported a positive association between Gammaproteobacteria abundances and vaginal delivery at ≤ 2 weeks of postnatal age. This difference was mainly attributed to PT infants belonging to a cluster of colonization characterized by high abundances of Gammaproteobacteria.35 In accordance with this, La Rosa et al. found that the abundances of Gammaproteobacteria were negatively associated with C-section delivery only in infants born less than 26 weeks of GA.48 It was also reported by two different authors that the abundance of Bacilli was greater in PT infants delivered via C-section.48 Furthermore, vaginal delivery was positively associated with the abundances of Bacteroides,49–51Parabacteroides,44Staphylococcus,38 and Enterobacteriaceae,52 and was negatively associated with the abundances of Enterobacter, Pantotea, Kluyvera, Erwinia, Klebsiella32 and Clostridium.52 Differences between mode of delivery and gut microbiota composition seem to be more pronounced soon after birth, and diminish over postnatal time. A longitudinal study over the first 100 days of life of PT infants reported that during the first week after birth, vaginally delivered infants belonged to a bacterial cluster dominated by Escherichia, and infants born via C-section were more likely to associate with a cluster dominated by Klebsiella.40 Although these differences remained similar during the first four consecutive weeks of postnatal age, after the fifth week, both groups  showed similar patterns of colonization.40 Finally, a total of four studies found no differences in gut microbial composition and mode of delivery.53–55Thirteen publications reported significant modifications in the taxonomic profile of PT infants depending on mode of delivery. A case-control study looking at the effect of histamine-2 receptor (H-2) blocker, found that Proteobacteria abundance was significantly lower in vaginally-delivered infants compared to infants born via C-section.35Few data exist regarding associations between ethnicity and sex and the gut microbial colonization of PT infants, as shown in . A longi33 As for beta diversity, sex explained 6% of the variance from the Bray-Curtis dissimilarity index.33 In contrast, two publications found no differences in alpha diversity,42 and beta diversity34 associated with infant’s sex.In terms of differences in microbiota by infant sex, Cong et al. showed that alpha diversity, measured by the Gini-Simpson diversity index, was positively associated with female sex.56 Gregory et al. reported that, after birth, there were significant differences in beta diversity between PT infants with extremely low birth weight  and PT infants with very low birth weight .57 This same study, observed differences in the taxonomic composition by birth weight. However, these differences were primarily observed in infants fed PT formula. Across time, the abundances of Lactobacillales were higher in ELBW infants compared to VLBW infants. In contrast, the abundance of Clostridiales and Enterobacteriales was greater in VLBW across time compared to ELBW.57Four studies, summarized in , reporte58 The authors compared PT infants that presented growth failure (weight below the 3rd percentile of the Fenton growth charts) at 40-weeks postmenstrual age and PT infants with appropriate growth. In the first nine weeks postnatal age, alpha diversity (Shannon diversity index) was lower in infants with growth failure.58 Infants that had growth failure had higher abundances of Staphylococcaceae and Bacteroideceae during the first weeks postnatal age, but during the third and ninth week of life, PT infants had greater abundance of Enterobacteriaceae and Erysipelotrichaceae.58 In the appropriate postnatal growth group, the authors found significant differences over time  in bacteria of the family Bacillaceae, Streptococcaceae, Peptostreptococcaceae, Veillonellaceae, Lachnospira-ceae, Micrococcaceae, Tissierellaceae and Clostridiaceae.58 Furthermore, this same group created a gut microbiota maturity index to investigate its association with growth. The final model of this maturity index included the following discriminatory bacteria: Lactobacillales, Peptostreptococcaceae, Clostridiaceaceae, Streptococcus, Staphylococcus, Veillonella, Enterococcus, Rahnella, Bifidobacterium, and Erwinia.58 Even though the relative microbiota maturity index was positively correlated with postmenstrual age, infants with growth failure had significantly lower values of this index compared to infants with appropriate growth.58A longitudinal study analyzed the association between growth and gut microbial colonization.59 Dahl et al. analyzed the gut microbiota composition of PT infants at three different time points: 10 days, 4 months, and 1 year after birth. The authors found that Shannon diversity index was positively associated with GA at birth during the first 10 days postnatal age, even after controlling for exposure to antibiotics.59 Similar results were found by Chernikova et al. where after adjusting for postnatal age, antibiotic use, delivery mode and consumption of human milk, extremely PT infants (born <28 weeks GA) had significantly lower alpha diversity (measured by the Simpson diversity index) compared to very PT infants (born 28–32 weeks GA) and to moderate/late PT infants (born 32–37 weeks GA).50 Whereas alpha diversity was similar between very and moderate/late PT infants.50 Therefore, the authors created two groups of infants based on birth GA: infants born before 32 weeks GA, and those born ≥32 weeks GA. Infants born at a later age had higher Simpson diversity index compared to those born before 32 weeks of gestation.50 A large number of longitudinal observational studies reported that alpha diversity, measured by different indices, increases with postnatal age.61A total of 28 studies, shown in , reporte37 Results showed that GA at birth explained 1.28%37 to 3%33 of the variance of the Bray-Curtis dissimilarity index. The former came from infants exclusively fed MOM,37 whereas the latter was independent of the feeding type.33 Four different observational studies looked at gut microbiota development of PT infants, with a follow-up period of the first 21,62 30,43 or up to 6057 days of life. These studies found that postnatal age significantly associates with the community structure measured by UniFrac distances,62 and the Bray-Curtis dissimilarity index.57Eight studies reported the effect of birth GA and postnatal age on beta diversity. Two longitudinal studies explored gut microbial colonization of PT infants based on type of feeding during the first days of postnatal age.63 At phylum level, the authors found that in healthy infants, there is a decrease in the abundances of Proteobacteria over time.63 Evidence from multiple longitudinal studies suggests that during early time points after birth, there is an enrichment of Bacilli,64 which then decreases over time.35 This decrease in Bacilli coincides with an enrichment of Gammaproteobacteria,64 and Clostridia.64 In accordance, Gregory et al. showed that from 28–30 weeks of corrected GA, the gut microbiota is characterized by higher abundances of Bacillales and Lactobacillales.57 Following this, there is a significant decrease in Lactobacillales, particularly in infants fed PT formula.57 Around 31–33 weeks of corrected GA, in infants fed PT formula, there is a bloom of Enterobacteriales, and in infants fed PT formula plus MOM a bloom of Clostridiales.57 At family level, authors reported that during early-life time points  there are higher abundances of Comamonadaceae,65 and Gram-positive cocci55 such as Staphylococcaceae.58 Bacteria from the families of Enterobacteriaceae65 and Bifidobacteriaceae55 also increase their abundance over time.Twenty-four studies included in this systematic review reported differences in taxonomic composition based on postnatal age. Mai et al. conducted a case-control study comparing PT infants with late onset sepsis to healthy PT infants.Staphylococcus,66 and Enterococcus67 are the main colonizers of PT infants gut during the first weeks of life . Zwittink et al. showed that at three weeks postnatal age, the mean relative abundance of Staphylococcus and Enterococcus was higher in extremely PT (<28 weeks GA) infants compared to very and moderate/late (32–37 weeks GA) PT infants.45 Following the first weeks of life, some studies report a decrease in the abundance of Staphylococcus,62Escherichia-Shigella,32Streptococcus,52 and Parabacteroides.50 Furthermore, there is a positive association between postnatal age and the presence and/or abundance of specific bacteria, including Anaerobiospirillum,66Haemophilus,66Veillonella,66Lactobacillus,50Bacteroides,51 Clostridia,66Serratia,66Yersinia,66Pseudomonas,66Klebsiella,52Granulicatella,66Proteus,66Propionibacterium,56 and Enterobacter.38 Zhou et al. reported that in their study population, Enterobacter was a member of the core microbiota of PT infants over the first 60 days of postnatal age.42 However, these results are still not consistent. Moles et al. found a decrease in the abundance of Propionibacterium from meconium samples to stool samples during the third week of life.66 It is important to highlight that although studies report an increase in the abundance of Bifidobacterium over time,68 evidence converges in that the colonization with this obligate anaerobe is delayed in PT infants.55At lower taxonomic rank, bacteria of the genera Streptococcus and Bifidobacterium than those born ≤32 weeks GA.50 At 6-weeks of postnatal age, infants born >32 weeks GA had a higher number of members from the genera Bacteroides and lower abundance of Parabacteroides.50 This same study also reported that the abundance of Pantoea, a bacteria of the family Enterobacteriaceae, was higher in moderate/late PT infants (32–37 weeks GA), even after adjusting for other exposures including postnatal age.50 Another longitudinal study found that at three weeks postnatal age, extremely PT infants (<28 weeks GA) had higher abundances of Enterobacter, whereas very PT infants (28–32 weeks GA) harbored higher abundances of Bifidobacterium.45The colonization pattern across time in PT infants is also affected by GA at birth. Chernikova et al. reported that infants born >32 weeks GA are colonized with greater abundances of 61 or PCR-amplified 16S rRNA fragments.66 There was a positive association between postnatal age and the abundance of Enterobacter aerogenes,61Bacteroides splachnicus,66Escherichia coli,61Clostridium difficille,66 and Klebsiella pneumoniae.66 Whereas, over time, there was a decrease in Prevotella tannerae, Lactobacillus plantarum, Streptococcus intermedius, and Streptococcus mitis.66Less data exists regarding the relationship between specific species and postnatal age. Two longitudinal studies analyzed the gut microbiota of PT infants at species level utilizing human intestinal tract chip analysis70 Two observational studies determined that the duration of antibiotic exposure was significantly associated with the reduction in microbial diversity.71 The decrease in alpha diversity was similar in PT infants that were exposed to short antibiotic treatment, ranging from ≤ 3 days34 to ≤ 7 days,71 or exposed to longer treatment (≥ 5 days or > 7 days).71 Furthermore, the reduction in alpha diversity seems to be only temporal. Several studies reported that a decrease in diversity indices like observed OTUs, Simpson, Shannon, Chao1, and phylogenetic diversity remains significant only within the first week after the use of antibiotics.70 In fact, diversity tends to recover after the cessation of antibiotic treatment.34 Nonetheless, some studies reported no effect of antibiotics in diversity metrics38 or opposite results41 than those previously described in this review. Wandro et al. conducted a longitudinal study of VLBW (<1500 g) infants and found decrease Shannon diversity index in PT infants with no record of antibiotic use.41Twenty-two studies reported the effect of antibiotics on the gut bacterial communities of PT infants, presented in . As expe45 Cong et al. observed that antibiotic use within the first 48–72 hours after birth explained ~3% of the variation from the Bray-Curtis dissimilarity index.33 Evidence from two studies conducted by Zwittink et al. described a strong association between antibiotic treatment and beta diversity.45 The duration of the use of antibiotics, whether it was less than three days or more than five days, explained 3.6% of the variation of the gut microbiota composition.34 Furthermore, up to 25.6% of the variance of these bacterial communities was explained when more antibiotic-related factors were taken into consideration, such as duration and number of antibiotics that were administrated.45Several studies found significant associations between the use of antibiotics and beta diversity of gut microbiota from PT infants.64 and Betaproteobacteria,71 while there was a negative association with bacteria from the class Clostridia.72 A study found that, at 30 days postnatal age, PT infants who were never exposed to antibiotics, had higher abundances of Bifidobacteriaceae, Streptococcaceae, Comamonadaceae, Staphylococcaceae, and unclassified Bacilli compared to infants that have been previously exposed to antibiotics.65 Millar et al. reported that by each successive day of antibiotic usage in PT infants, there is 16% to 17% less chance of colonization with Veillonallaceae.73 In accordance with this, another study reported that exposure to antibiotics reduces the presence of bacteria from the genus Veillonella.53 Different studies reported a negative association between the exposure to antibiotics and the abundance of Enterobacteriaceae,34Lactobacillus,53Bifidobacterium,71 and Bacteroides;50 and a positive association between antibiotic use and the abundance of Enterococcus.34 However, a study looking specifically at Bacteroides gut colonization in PT infants found no association between antibiotic use and the abundance of this bacteria.51Changes in diversity induced by antimicrobial agents in PT infants subsequently influence taxonomic composition of the fecal microbiota, with some bacteria decreasing while others blooming. Specifically, there was a positive association between the exposure to antibiotics and the abundance of GammaproteobacteriaStaphylococcus epidermis after the use of meropenem (beta-lactamase inhibitor), Klebsiella pneumoniae after the use of Ticarcillin-Clavulanate (combined extended-spectrum penicillin with a beta-lactamase inhibitor), and Escherichia coli with the use of cefotaxime, a broad spectrum cephalosporin antibiotic.60 All of these medications exert their antibiotic effects by affecting cell wall synthesis, or by causing cell death. A similar study conducted by Zhu et al. reported an increase in bacteria of the phylum Bacteroides and Actinobacteria with the use of penicillin-moxalactam  and with the use of piperacillin-tazobactam (penicillin with a beta-lactamase inhibitor).70 With penicillin-moxalactam, PT infants had greater abundances of Sphingomonas, Bacteroides, and Lactobacillus, and a decrease in Clostridium.70 Korpela and collaborators found a decrease in Bifidobacterium abundances when an antibiotic of the class aminoglycosides or vancomycin were used.38 This same study also described that when antibiotics of the class of aminoglycosides were administered, the abundance of Enterococcus decreased. In contrast, when vancomycin was used, the presence of Enterococcus was higher.38 Although the taxonomic modifications were significant, these were only temporal, and the microbiota structure recovered within days after the cessation of antibiotic treatment.38Importantly, microbiota modifications caused by antibiotics might depend on the type of antibiotic used. Gibson et al. assessed the effect of different antibiotics, including meropenem, cefotaxime, ticarcillin/clavulanate, ampicillin, vancomycin, and gentamicin. They reported an increase in 74 Bacteria isolates obtained from stool samples were assessed for antibiotic susceptibility using agar dilution assays. This assay consists in platting the isolates in agar medium with antibiotics and measuring the diameters of the colonies that were exposed to the antibiotic.75 The authors also performed bacteria identification at species level using MALDI-TOF spectrometry. In the early postpartum period, PT infants were colonized by a number of antibiotic-resistant bacteria including Enterococcus faecalis, Enterococcus faecium, Staphylococcus aureus, Escherichia coli, and Klebsiella pneumonia.74 However, by 2-years of age, these same bacteria showed antibiotic susceptibility.74Even though antibiotics are used to treat or reduce the presence of pathogenic bacteria in PT infants, their efficacy could be blunted by the presence of antibiotic-resistant bacteria. Moles et al. evaluated the gut colonization of PT infants by antibiotic-resistant bacteria during the first week of life and at 2-years of age.47 There was a decrease in the alpha diversity (measured by Shannon diversity index) in PT infants that were exposed to H2-blockers compared to those not exposed to this medication.47 Taxonomically, after the administration of H2-blockers, there was a significant decrease in Firmicutes accompanied by an increase of Proteobacteria. At lower taxonomic ranks, infants that were exposed to H2-blockers had increased abundance of Gammaproteobacteria and Enterobacteriaceae.47 Additionally, an observational study found a positive association between the use of H2-blockers and the abundance of Bifidobacterium at a later time point of the follow-up period .64Only a few studies have evaluated the effect of medications other than antibiotics on the gut colonization of PT infants, as shown in . Gupta e64 At 33-weeks postmenstrual age, there was an increase of Bifidobacterium abundance associated with greater ratio of enteral lipid intake whereas, and higher ratio of enteral protein intake was associated with reduced Bifidobacterium.64Several studies, summarized in , have re76 There were no differences in the first week after supplementation with HF-PUFA, but over time, alpha diversity (measured by Shannon and inverse Simpson indices) was higher in infants that received HF-PUFA supplementation.76 At phylum level, those receiving the HF-PUFA intervention had a lower abundance of Proteobacteria and higher abundance of Actinobacteria than those without the intervention.76 There were further differences at the genus level, and these differences were categorized as early (1–9 weeks), mid (2–9 weeks), or late (4–9 weeks) changes after treatment initiation. Some of the early changes were a decrease in Escherichia-Shigella and Salmonella in the HF-PUFA, and an increase in the abundance of Corynebacterium and Geobacillus. At 2–9 weeks after initiation of HF-PUFA, supplementation, there was a significant increase in the relative abundance of Erwinia and decreases in Serratia, Pantoea, Clostridium, Tatumella, and Streptococcus. Lastly, a reduction in fecal Cedecea and Citrobacter in the HF-PUFA group was reported as a late change.76Younge et al. conducted a randomized controlled trial (RCT) to test the effect of enteral supplementation of high-fat polyunsaturated fatty acid (HF-PUFA) from fish oil and safflower oil on the gut microbiota of PT infants.33 Gibson et al described an increase in species richness with the consumption of human milk .60 Another study reported that the combination of MOM with a bovine milk-based fortifier, two to four weeks after the introduction of enteral feeds, significantly increased alpha diversity compared to PT infants fed PT formula alone or in combination with MOM.46 Underwood et al. found a lower Shannon diversity index in infants fed PT formula compared to those fed MOM.30 Additionally, one study reported that introduction of PT formula before 10 days postnatal age was positively associated with Shannon diversity index.59 Nonetheless, some studies found no differences in alpha diversity based on the type of diet, whether it was human milk (MOM and/or DHM),77 PT formula,77 the combination of human milk and PT formula,41 or the supplementation of human milk with a bovine milk-based fortifier.33Twenty studies reported associations related to milk and/or fortifier administration to PT infants and the structure of their gut microbiota, shown in . One stu78 and UniFrac distances.78 Another study comparing only human milk vs. PT formula found an association between these feeding exposures and the gut microbiota structure.79 Cong et al. compared the effect of different types of milks and found that up to 11% of the variance in the Bray-Curtis dissimilarity index could be explained by the feeding type.33 However, some studies found no association between human milk consumption and the gut microbiota beta diversity of infants born preterm.45Six different studies described the effect of milk and fortifiers on beta diversity. There was a positive association between feeding type  and beta diversity, measured by Bray-Curtis dissimilarity index59 and the fortification with a bovine milk-based fortifier,46 as well as a negative association between MOM consumption and the abundance of Firmicutes.59 A study conducted by La Rosa et al. reported that across time, there is a linear relationship between the abundance of Gammaproteobacteria and MOM consumption.48 et al. found a decrease in the abundance of Lactobacillus with exposure to human milk (MOM and/or DHM).50 Butcher et al. followed PT infants that were exclusively fed MOM and identified that these infants were mainly colonized by Bacilli, Clostridia, and Gammaproteobacteria, with very low levels of Bifidobacterium.37 In contrast, different studies showed that exposure to MOM was associated with greater abundances of bacteria of the class Lactobacillales,33 Bacillales,33 Bifidobacteriales,33 and Clostridiales,57 and higher abundance of the genera Bifidobacterium, Acinetobacter and Haemophilus.78 One study compared the gut colonization specifically by species of the genus Staphylococcus in PT infants exclusively fed MOM.80 Results showed that infants fed MOM had a lower presence of Staphylococcus aureus, Staphylococcus hominis, and Staphylococcus lugdunesis compared to full-term infants.80 Some of these differences could be attributed to the composition of human milk. Underwood et al. reported that PT infants consuming MOM of secretor mothers (expressing 2′-fucosyltransferase) that produce milk containing the human milk oligosaccharides (HMOs), 2′-fucosyllactose and lactodifucotetraose, had a lower abundance of Gammaproteobacteria and higher abundance of Lactobacillaceae.81 Another observational study analyzed the gut microbial composition of PT infants and of the milk they were fed.68 The authors reported that when infants consumed MOM with a high abundance of Staphylococcus, they harbored a gut microbiota rich in Staphylococcus.68 In contrast, infants that consumed MOM high in Rothia, Enterococcus, and Streptococcus developed a gut microbiota with higher abundances of Bifidobacterium.68Taxonomically, studies found a positive association between the abundance of Proteobacteria with MOM consumption33Staphylococcus, Clostridium, Coprococcus, Aggregatibacter, and Lactobacillus.78 If infants were exclusively fed PT formula, they had a greater abundance of Lactobacillales,57 Enterobacteriales,57 and Clostridiales57 compared to those fed human milk. At genus level, PT formula consumption was positively associated with the abundance of Blautia, Streptococcus, Acidaminococcus, Rothia, Dorea,78Terrisporobacter and Peptoclostridium.46 An observational study concluded that the best discriminators of the gut microbiota of PT infants fed MOM were bacteria of the genus Citrobacter, Clostridium, Ruminococcus, and Negativicoccus, whereas the best discriminators of infants consuming PT formula were Streptococcus, Bacillus, and Anaerococcus.57Studies also reported differences in the gut microbiota based on DHM and PT formula consumption. Preterm infants that were fed DHM had higher abundances of Enterobacteriales,33 Infants consuming MOM in combination with PT formula, had the highest enrichment of Bifidobacteriales.33 Furthermore, studies also observed differences in the gut microbiota depending on the type of fortification to human milk. Cai et al. described a decrease in Veillonella with a bovine milk fortifier in infants consuming MOM.46 Another study found that PT infants fed MOM with human milk-based fortifier had a higher abundance of Gammaproteobacteria and a lower abundance of Bacillales.28 Additionally, an increase in Bifidobacterium was observed if PT infants were fed MOM with the addition of a human milk-based fortifier.28 In contrast, Butcher et al. did not find changes in the gut microbiota of PT infants when they received MOM fortified with a bovine milk-based fortifier.37Although there are differences in gut microbiota composition depending on the type of milk consumed, PT infants are likely to be fed more than one type of milk at a time. As previously mentioned, a study evaluating different feeding patterns found that when infants were fed DHM and/or PT formula, they had increased levels of Enterobacteriales compared to other feeding groups and combinations.83 probiotics,84–86 or both.87 The prebiotics tested on PT infants were fructooligosaccharides (FOS) from clover honey,82 galactooligosaccharides (GOS) + FOS,83 GOS vs. HMOs,28 or GOS + FOS + acidic oligosaccharides (AOS).69 Publications related to prebiotic supplementation did not report differences in alpha or beta diversity subsequent to supplementation. However, several taxonomic differences were found with prebiotic supplementation. When PT infants received FOS alone, there was a decrease in Enterobacteriaceae and an increase in Bifidobacterium and Lactobacillus.88 With the supplementation of GOS + FOS, the abundance of Lactobacillus increased over time; however, the abundance of Lactobacillus was not significantly different than the control group.83 Despite the changes mentioned above, Westerbeek et al. did not find any significant changes in the gut colonization of PT infants after intervention with a mixture of GOS + FOS + AOS.69 Underwood et al. reported an increase in Clostridia with increasing doses of either GOS or HMOs added to PT formula compared to PT formula without these prebiotics.28Fourteen studies described the effect of prebiotics or probiotics in the gut microbiota of PT infants, presented in . These sLactobacillus acidophilus + Bifidobacterium bifidum),53 a mixture of Bifidobacterium breve + Bifidobacterium longum subsp. infantis + Bifidobacterium longum subsp. Longum,84 single strain administration of Bifidobacterium breve,84Bifidobacterium longum BB536 + Lactobacillus rhamnosus GG,86Lactobacillus rhamnosus,85Bifidobacterium breve M16-B,29Bifidobacterium longum subsp. infantis or Bifidobacterium animals subsp. lactis,30 or Dierol® (Saccharomyces boulardii).44 After supplementation with Infloran®, there was an increase in the alpha diversity, measured by Shannon diversity index.27 Both publications supplementing Infloran® to PT infants reported an increase in Lactobacillus and Bifidobacterium after treatment.53 Additionally, in PT infants supplemented with Infloran®, there was an increase Escherichia, along with a decrease in Veillonella, and Streptococcus at 28 days postnatal age.53 The treatment with B. breve showed no impact on the alpha diversity (measured by Simpson diversity index) after supplementation.73 Administration of B. breve alone or in with B. infants + B. longum increased the abundances of Bifidobacterium in the gut of PT infants.84 When infants received B. longum + L. rhamnosus there was an increase in Bifidobacterium as well as in Lactobacillus.86 Underwood et al. found an increase in the Shannon diversity index when formula-fed PT infants received B. animalis subsp. lactis.30 This study also found an increase in Bifidobacterium after supplementation with B. longum subsp infantis in infants consuming PT formula; however, this increase was even greater in PT infants receiving the supplementation while consuming MOM.30 There was no dose-response in Bifidobacterium abundance in the group supplemented with B. animalis subs lactis.30 Overall, the supplementation with probiotics decreased the presence of Proteobacteria,30Enterobacteriaceae,84 and Clostridium histolyticum.85 A study found that after supplementation with B. breve, the gut microbiota of some PT infants had less than 6% abundance of Bifidobacterium; these infants were described as non-responders.29 Additionally, these infants actually had increased abundances of Enterobacteriaceae and Clostridiaceae.29 After supplementation with Dierol®, there was a decrease in the alpha diversity (measured by Simpson diversity index) overtime. However, this reduction was not significantly different than the one showed in the placebo group.44 The authors found no association between probiotic supplementation with Dierol® and the gut microbiota beta diversity. Dierol® supplementation was associated with an increase in Veillonella, Clostridium, and Bifidobacterium, as well as a decrease in Enterococcus and Pseudomonas.44The probiotics that were supplemented in the studies included in this review include: Infloran® (L. rhamnosus GG + inulin) or to ProBioPlus DDS® (L. acidophilus + B. longum + B. bifidum + B. infantis + inulin). Over four weeks of supplementation, infants exposed to Culturelle® showed a decrease in Gram-negative bacteria, whereas supplementation with ProBioPlus DDS® significantly increased Bifidobacterium and Lactobacillus over the same 4-week period.87One study aimed to analyze the effect of the combination of prebiotic and probiotics. Preterm infants were exposed to Culturelle® .7 Thus, further research is needed to clarify the results.In full-term infants, significant differences in fecal microbiota have been reported depending on the mode of delivery,42 There was not a significant trend that could be drawn from these results. Although previous literature has reported possible differences in the gut microbiota associated with sex104 and ethnicity,105 insufficient data exists in newborns. There is a strong relationship between PT birth and low birth weight which can affect the fecal microbiota composition. Low birth weight appeared to be associated with higher abundances of Lactobacillales and Clostridiales.57 However, these results could be in part explained by the feeding regimen these infants were exposed to, since these findings came from PT infants fed PT formula. In fact, evidence has shown that there are higher abundances of Clostridium and Lactobacillus in full-term infants fed formula.106 In addition to low birth weight, postnatal growth failure is a common feature in PT infants. A study reported the association between diversity, microbiota maturity and growth failure.58 A low microbiota-for-age Z-score was found to be prevalent in PT infants.58 In children, the microbiota-for-age metric has been linked to modifications in the taxonomical composition related to malnutrition.107Different physiological factors were explored in this review, including ethnicity, sex, weight, and age. Few studies reported the effect of genetic factors like ethnicity and sex affecting the gut microbiota of PT infants.Enterococcus, Bacilli and Staphylococcus and an increase in Enterobacteriaceae, Clostridia and Bifidobacterium. It is important to highlight that the colonization with Bifidobacterium appears to be delayed in PT infants compared to full-term infants.55 Studies in full-term infants have shown that the gut bacterial communities are characterized by low diversity after birth which increases over time and is influenced by dietary factors such as breastfeeding and weaning.108 Immediately after birth, the primary gut colonizers are facultative anaerobic bacteria, which reduce the oxygen content of the gut to allow for the subsequent colonization with obligate anaerobes.109Clostridium is a strict anaerobe, and some of its members, particularly those from Clostridium cluster 1, are associated with prematurity and NEC.110 In contrast, the late acquisition of Bifidobacterium in PT infants, another strict anaerobe, could be attributed to the lower exposure to human milk compared to full-term infants. The colonization with Bifidobacterium has been significantly associated with breastfeeding and human milk consumption in newborns.111In PT infants, both GA at birth and postnatal age are associated with modifications in the structure of the gastrointestinal microbiota. Throughout the literature review, there was consistent evidence of an increase in diversity with greater GA at birth and postnatal age. The most notable changes over time were a decrease in Bifidobacterium78 and Staphylococcus.78 This association could be explained in part to the microbiota composition of human milk. Studies have found that the microbiota composition of human milk is rich primarily in Staphylococcus113 and Bifidobacterium.112 Nonetheless, there was no consistency across studies between types of milk (human milk [MOM or DHM] or PT formula) and taxonomic composition of the fecal microbiota of PT infants. This could be explained, in part, by the variety of feeding strategies PT infants are exposed to during the hospitalization period. Rates of breastfeeding are lower in PT infants compared to full-term infants;114 mothers that deliver prematurely may have little or no milk production caused by immaturity in the mammary gland, illness, or stress.114 In the case that PT infants do not receive their MOM, they will be fed DHM or PT formula. Differences in the composition of DHM and PT formula could result in very different gastrointestinal colonization patterns in PT infants. Furthermore, if PT infants are fed MOM or DHM, this will be supplemented with a milk fortifier (human milk-based or bovine milk-based) to achieve adequate nutritional composition and meet the newborn’s needs.115 These fortifiers likely further alter gut microbiota composition. With these diverse feeding possibilities, significant and consistent changes will be less likely to be found. For instance, although Bifidobacterium is associated with human milk consumption, one study reported a decrease of this bacteria when PT infants were exposed to MOM fortified with a bovine milk fortifier.28 This underlines the importance of studies analyzing the gut microbiota taking into account and reporting detailed information regarding the infant’s diet. Additionally, the majority of studies from this literature review were related to dietary factors focused on human milk and/or fortifiers, and very few considered the effect of macronutrients.76 Results from two studies showed that the protein content, lipid content,64 and lipid supplementation76 of the diet are associated with the fecal microbiota of PT infants, but more well controlled RCT are needed to further explore these conclusions.The strong relationship between diet and fecal microbiota composition is a well-known fact. Human milk is the gold standard for infant nutrition and plays an essential role in the gut bacterial colonization. The review of the literature showed that in PT infants, this is not an exception. Overall, the consumption of human milk, particularly MOM was associated with greater presence of B. breve, B. longum, L. acidophilus, and L. rhamnosus. Most of the significant modifications in the gut microbiota of PT infants were observed with the use of probiotics. As expected, there was a noticeable increase in Bifidobacterium and Lactobacillus in the infant’s gut with the use of probiotics. Interestingly, the use of probiotics together with human milk (particularly MOM) had an additive effect in increasing Bifidobacterium abundance.30 Human milk is rich in HMOs,116 which are indigestible carbohydrates that are utilized by members of the genera Bifidobacterium.117 This could explain the differences between PT formula-fed and PT infants exposed to MOM. The use of prebiotics and/or probiotics has shown to decrease colic episodes, decrease fecal pH, improve feeding tolerance and gastric motility, and reduce the risk of allergies.118 Extensive research has shown the beneficial effect of probiotics therapy in the reduction of NEC and death in infants born preterm.119 However, evidence is still lacking regarding the short- and long-term effects that these probiotics have in the fecal microbiota in PT infants.Several studies from this review reported changes in the gut microbiota with the consumption of prebiotics and probiotics, whether used separately or in combination. The most commonly prebiotics administrated were FOS and GOS, and the most common probiotics were 120 Across the literature, studies found modifications in the diversity and the taxonomical composition of the gut microbiota populations in PT infants after the exposure to antibiotics. Even though antibiotics are prescribed to reduce the number of pathogenic bacteria, the literature review showed this comes accompanied by a decrease in beneficial commensal bacteria like Bifidobacterium. The shifts in the overall structure of the gut microbiota are important for the host’s health in the sense that they could cause perturbations in the innate and adaptive immune system.121 This is something particularly significant for infants in a fragile state such as PT infants. Moreover, shifts in the gut microbiota appeared to be temporal. This goes in accordance with previous reports showing the transient modifications in the gut microbiota caused by antibiotics exposure, albeit the alteration in the immune system can still occur.122 Furthermore, antibiotic-associated alterations in the gut microbiota seem to be dependent on the type of antibiotic. One study reported opposite effects in the abundance of Enterococcus; this bacteria decreased with aminoglycoside and increased with vancomycin.38 Although vancomycin is used to treat gastrointestinal infections, vancomycin-resistant Enterococcus is common nowadays and can be the cause of serious infections in older population.123 Antibiotic-resistant bacteria infections have become a public health concern, and data have shown that infants can be colonized with antibiotic-resistant bacteria early in life.124 This colonization with antibiotic-resistant bacteria could be coming from environmental, dietary, or maternal factors.124 Further exploration of these associations should be conducted to understand the shifts in the bacteria communities of the PT gut. This review also aimed to describe the effect of all commonly administered medications on the gut microbiota composition of PT infants. However, only reports on antibiotics and H2-blockers were found and included in the results. Since PT infants are routinely exposed to a variety of medications during their stay in the NICU, which may modulate the microbiota composition and/or function, additional research that investigates the impact of these other medications is warranted.The use of antibiotics and the effect they have on the gut microbiota was widely reported across the literature. During hospitalization in the NICU, PT infants are exposed to a variety of medications and antibiotics. It has been reported that up to 89% of preterm infants received antibiotics after birth.55 Only one study found an effect between the infant transition from an incubator to an open bed and colonization with Bifidobacteriaceae.55 Two different studies, reported how the same bacteria strains were colonizing both the PT gut and many surfaces from the NICU.90 These similarities between housing environment and the microbiota from different parts of the human body have been previously studied. It has been hypothesized that humans might serve as vectors among multiple room surfaces, and thus, the colonization can be bi-directional.125 Hospital-acquired infections are strongly associated with the diversity of microorganisms found in this environment.126 This is particularly relevant for PT infants, since these infants are more likely to spend extended periods of time in the NICU. In this environment, a variety of surfaces could serve as sources of microorganisms, including incubators, ventilators, warmers, electronic equipment, as well as health care providers.22Finally, we considered the relationship that exists between the living environment and the gut microbiota. Although the evidence is scarce, two different studies demonstrated associations between the housing environment  with the structure of the gut microbiota of PT infants.Supplementary Table 1, the most common method used was NGS (47 studies); from these studies, three performed whole genome sequencing, whereas the rest used 16S rRNA sequencing. Other studies used bacterial culture, molecular methods and other non-sequencing methods such as qPCR, DGGE, TGGE, T-RFLP, PFGE, FISH, MALDI-TOF, and microarrays. The main limitation of bacterial culture and non-sequencing methods is an imprecise characterization of the microbiota diversity.127 Also, studies that utilize these methods usually target specific bacteria to answer specific questions, rather than assessing the gut microbiota in a broader way.128 From the studies that used NGS, there were also differences on the platforms used: pyrosequencing, Illumina dye sequencing or pH-mediated sequencing. The main differences between these techniques are related to read length, reads per run, and reads retained after filtering; where platforms like Illumina will yield more reads and longer reads than the other two platforms.129 Thus, this variation in gut microbiota assessment could create biases in the results that have been reported in this review.This review considers the multifactorial colonization of the PT gut, however, there are some limitations worth mentioning. One limitation of this review is the heterogeneity in the methods for the assessment and analysis of the gut microbiota across studies. As shown 130Lastly, the available literature regarding the gut microbiota of PT infants relied predominantly on observational studies with very few clinical trials, suggesting the need for more intervention RCTs with adequate power and sample size calculations. After birth, PT infants are at a fragile stage and require a variety of medical interventions. The exposure to different dietary and pharmacological factors will depend on the health status of the infant; this makes it challenging to conduct RCTs in this population. From the review of the literature, almost all the studies of prebiotics and/or probiotics supplementation were clinical trials. These microbiome modulating strategies have been widely used in PT infants and recently, the European Society for Pediatric Gastroenterology Hepatology and Nutrition (ESPGHAN) recommended additional RCT to study the effect of probiotics in infants born preterm.Bifidobacterium, Staphylococcus, Enterococcus, Gammaproteobacteria, Bacteroides, Streptococcus, Lactobacillus, Enterobacteriaceae, Escherichia and Clostridia. The results of this systematic review also illustrate the variability in some of the associations that have been reported with the gut microbiota, which highlights the need of more comprehensive studies analyzing the effect of mode of delivery, sex, type of milk consumed, use of fortifiers, and use of medications on the composition of the gut microbiota of PT infants. Infants born preterm most likely will be affected by multiple conditions at the same time including C-section delivery, antibiotics exposure, low birth weight, and different feeding regimes. With the rapid advancement in sequencing technologies, such as long-read 16S rRNA sequencing that allow for a deeper resolution of the gut microbiome, coupled with the use of more sophisticated computational tools, future biomedical research should aim to integrate multiple biological inputs, seeking to understand complex systems such as the gut microbiota of PT infants. From a systems biology perspective, this would encompass studying the associations between bacterial genome, infant’s metabolome, immune markers, clinical status, dietary factors, and the effect on the infant health outcomes. Robust associations support the need for prospective RCTs to utilize modifiable factors, such as diet, to mitigate the adverse effects of non-modifiable factors, including low GA or low birth weight, to help prevent or ameliorate detrimental complications associated with the common dysbiosis associated with PT birth.Results from this literature review about the multifactorial colonization in the PT infant gut highlights how multiple factors and different exposures can differently modify the abundance or presence of bacteria from the same genera or class, as shown in . AlthougClick here for additional data file.


Simple regression analysis revealed age, use of β-blockers, prostaglandin, carbonic anhydrase inhibitors, total number of medications, and number of daily BAK-containing instillations were associated with a thinner epithelium. Multiple regression analysis revealed β-blockers, prostaglandin, and number of BAK-containing instillations were significant factors. Use of β-blockers and number of BAK-containing instillations were also associated with a thinner epithelium in the monotherapy subgroup analysis. CET was significantly smaller in patients with glaucoma receiving topical medications and was affected by the use of β-blockers, prostaglandin, and BAK.Corneal thickness measurement is important for assessing intraocular pressure in patients with glaucoma. This study investigated the changes in corneal epithelial thickness (CET) induced by antiglaucoma medications and explored the factors affecting CET measurement. CET was measured over a 9.0 mm diameter area by using Fourier domain optical coherence tomography in 125 patients with primary open-angle glaucoma and 125 age-matched controls without glaucoma. The influence of sex, age, benzalkonium chloride (BAK)-containing instillations, disease severity, and types and numbers of medications was analyzed using simple and multiple regression analyses. CET over 25 sectors was smaller in the glaucoma group than in the control group (mean difference of 4.2 µm in the central 2.0 mm zone; 52.8 ± 3.6 vs. 48.5 ± 3.9,  Measurement of corneal thickness has played an important diagnostic role in the assessment of intraocular pressure (IOP) in patients with glaucoma ,2,3. CenOcular surface discomforts are more common in patients with glaucoma than in those without glaucoma. Since patients with glaucoma often require the long-term use of multiple eyedrops, the possible adverse effects of these drugs on the ocular surface have been important topics of research ,15,16. PTherefore, we aimed to investigate the influence of sex, age, BAK-containing instillations, disease severity, and types and numbers of topical medications on CET and stromal thickness in patients with glaucoma.This retrospective cohort study included 125 patients with primary open-angle glaucoma and 125 age-matched controls without glaucoma who were evaluated between March 2019 and March 2021. A complete ophthalmologic examination, including slit-lamp examination, IOP measurement, corneal thickness measurement, visual field analysis, OCT, and fundus examination, was performed, and patients with any corneal pathologies, such as corneal opacity, corneal dystrophies, keratoconus, a history of contact lens wear, and prior ocular surgery within 6 months, were excluded. Patients with glaucoma treated using the same medication for at least 6 months were included in the analysis. The control group comprised individuals presenting for regular check-ups. Individuals using any topical eyedrops except antiglaucoma medications were excluded from both groups. The CET and total thickness data were obtained using the RTVue  FD-OCT system  with a corneal adaptor module working at 830 nm wavelength. CET maps were generated by an automatic algorithm and divided into a total of 25 sectors over a 9 mm corneal diameter: a central 2 mm zone, eight paracentral zones within an annulus between the 2 and 5 mm diameter rings, eight mid-peripheral zones within an annulus between the 5 and 7 mm diameter rings, and eight peripheral zones within an annulus between the 7 and 9 mm diameter rings. The CET standard deviation within the central 7 mm area generated by the software was recorded as thickness variability. Stromal thickness was calculated by subtracting the epithelial thickness from the corneal thickness in the corresponding area. All ophthalmic measurement data obtained from only one randomly selected eye of each individual were included for statistical analysis. The study protocol was approved by the review board of our institute (CR321015), and the study was conducted according to the tenets of the Declaration of Helsinki. The need for informed consent was waived because of the retrospective nature of the study.p < 0.05 were considered statistically significant.Statistical analyses were performed using IBM SPSS Statistics for Windows, Version 25.0 . Student’s t-test was performed to compare continuous variables between patients with glaucoma and the age-matched controls. Simple linear regression was used to determine the association of age, sex, number of daily BAK-containing instillations, and types and numbers of medications with CET and stromal thickness. Multiple linear regression analyses were then conducted to investigate the influences of the associated factors derived from the simple regression analysis on CET and stromal thickness. A subgroup of 56 individuals using only one eyedrop was further analyzed to validate the effect of individual medication and BAK-containing instillations. Fixed combination medication users were included in the monotherapy subgroup. Values with This study analyzed data from 125 patients with glaucoma with a mean age of 66.7 ± 11.0 years and 125 controls with a mean age of 65.1 ± 8.9 years . No diffSimple regression analysis indicated that age, visual field index, use of β-blockers, prostaglandin, carbonic anhydrase inhibitors (CAIs), α-agonists, total number of medications, and number of daily BAK-containing instillations were associated with a thinner epithelium. Age was inversely proportional to corneal stromal and epithelial thicknesses in the glaucoma group, but it was not associated with CET in the control group . Multiplp = 0.029), whereas the use of prostaglandin, CAIs, α-agonists, and BAK did not induce significant differences (p = 0.029) and number of BAK-containing instillations (R = 0.288, p = 0.031; To demonstrate the effect of individual eyedrops without confounding factors, 56 eyes that were receiving only one eyedrop were further analyzed. This analysis revealed no differences in CET, stromal thickness, CCT, and epithelial thickness variability between individuals divided by the presence of preservatives in the administered eyedrop . The useferences . HoweverThe anterior segment OCT imaging has attracted increasing attention because it is capable of indicating ultrastructural or functional changes of the ocular surface, which is useful for detecting corneal sublayer changes in various conditions such as keratoconus, laser refractive surgery, corneal crosslinking, orthokeratology, corneal dystrophies, and chemical burns ,27,28,29Given the increased prevalence of dry eye or ocular surface discomfort in patients with glaucoma, previous studies investigated the effects of IOP-lowering eyedrops on CET and reported smaller CETs in patients with glaucoma ,18,19. SThis study examined CET and stromal thickness in patients with glaucoma and aimed to detect factors that could affect the thickness profiles. When compared to an age-matched control group without glaucoma, the glaucoma group showed statistically significant reduction in CET in all 25 sectors over a 9.0 mm diameter area. Previous studies have reported reduced CETs in patients with glaucoma, but only few studies revealed significant factors affecting the reduction in CET ,18,19. MSurprisingly, no previous studies have evaluated the effect of the preservative BAK on epithelial thickness, and most studies have not reported the differential results based on the presence of BAK in the administered eyedrops. The current study therefore focused on the effects of individual medications and BAK on CET and stromal thickness. We identified that both the total number of medications and the severity of glaucoma were significant confounding factors because they correlated with many variables and were associated with reduced epithelial thicknesses. Our analysis of the monotherapy subgroup has therefore provided insights on the effects of individual medications and BAK. Our data demonstrated that β-blockers reduced CET and the number of BAK-containing instillations was significantly associated with epithelial thinning. An association between β-blockers and CET has also been observed in previous studies ,19. InteNotably, the presence of preservatives in monotherapy drugs did not induce any significant alterations in epithelial thickness , but it We also investigated the topographic epithelial thickness maps of the glaucoma and control groups. Overall, the topographic profiles were similar in the two groups, and previous studies reported that the superior and temporal epithelial thicknesses were smaller than the inferior and nasal epithelial thicknesses, respectively ,39. An iThis study has several limitations. First, this study did not include the duration of treatment as a variable. Since our hospital is a tertiary care center, only a limited number of individuals are newly diagnosed, and it is difficult to know exactly how long the patients have been on medications. However, previous studies have reported that there was no correlation between CET and glaucoma treatment duration ,19. SecoThe present study demonstrated that the CET profiles of patients with glaucoma receiving topical medications were uniformly reduced when compared to those of controls without glaucoma. The observed alterations were affected by the use of β-blockers, prostaglandin, and BAK, and the total number of medications and disease severity were confounding factors.