A Microbiota Profiling of Breast Milk in Relation to Gestational Diabetes Mellitus Status - A Greek pilot study

A Microbiota Profiling of Breast Milk in Relation to Gestational Diabetes Mellitus Status - A Greek pilot study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article A Microbiota Profiling of Breast Milk in Relation to Gestational Diabetes Mellitus Status - A Greek pilot study Sophia Letsiou, Despina Vougiouklaki, Simen Akkermans, Zoe Siateli, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7262551/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Gestational diabetes mellitus (GDM) is associated with metabolic alterations that may influence maternal and neonatal microbiota. While the impact of GDM on the infant gut microbiome has been increasingly studied, its effect on the breast milk microbiota remains poorly understood. In this pilot study, we compared the breast milk microbiota received from 25 women with GDM compared to that of 25 non-diabetic breastfeeding mothers to serve as controls using full-length 16S rRNA gene sequencing on the Oxford Nanopore platform. Significant differences in microbial composition were observed between the two groups. Breast milk from GDM mothers showed increased abundance of Pseudomonadota (formerly Proteobacteria ), including species such as Acinetobacter johnsonii and Bradyrhizobium mercantei , while beneficial Bacillota (e.g., Lacticaseibacillus paracasei ) were significantly reduced compared to non-GDM samples. These findings suggest that GDM may alter breast milk microbial composition in ways that could influence neonatal early-life microbial colonization and immune programming. Given the known links between dysbiosis and metabolic and immune-mediated diseases, our results underscore the need for longitudinal, multi-omic studies to elucidate the long-term health implications of GDM-associated shifts in the breast milk microbiome. Health sciences/Diseases Biological sciences/Microbiology Gestational diabetes mellitus microbiota breast milk microbiota profiing NGS analysis Figures Figure 1 Figure 2 Introduction Gestational Diabetes Mellitus (GDM) is a metabolic disorder arising during pregnancy in women without a history of diabetes and typically resolving following delivery 1 . It is the most common metabolic complication of gestation, affecting even up the 25% of pregnancies globally, with an incidence depending on the studied population and the diagnostic criteria applied 2 , 3 . According to the International Diabetes Federation report, the global incidence of GDM for certain European populations ranges from 1–14%, while its prevalence may be as high as 20–25% 4,5 . GDM is primarily characterized by increased insulin resistance status during the second and third trimesters of pregnancy. driven hormonally by the increased levels of the human placental lactogen (hPL), cortisol, estrogen, and progesterone in circulation. Insensitivity to insulin gradually prompts pancreatic β-cells to fail in compensating for the higher insulin demands and consequently maternal hyperglycemia ensues 6 , 7 . Key risk factors for GDM include maternal obesity, maternal age > 25 years, family history of diabetes, prediabetes, polycystic ovary syndrome (PCOS), a previous diagnosis of GDM, and Asian, African American, or Hispanic ethnic background, among others 7 – 9 . According to findings from the Hyperglycemia and Adverse Pregnancy Outcome (HAPO) study, maternal hyperglycemia is a key factor contributing to multiple metabolic disturbances in both the mother and fetus 10 – 13 . GDM has been associated with many adverse maternal and neonatal outcomes, including cesarean delivery, preeclampsia, preterm birth, large-for-gestational-age (LGA) neonates, shoulder dystocia, and neonatal hypoglycemia. Women diagnosed with GDM and their offspring face an increased long-term risk of developing type 2 diabetes mellitus (T2DM), obesity, cardiovascular disease, and other metabolic disorders. Emerging evidence also indicates that neonates of mothers with GDM are at elevated risk for immune-mediated conditions, such as atopic dermatitis and allergen sensitization. A clinical study reported that infants born to mothers with GDM exhibited more than a five-fold increase in the risk of allergen sensitization. Furthermore, these infants were more likely to develop atopic dermatitis, which itself is associated with a greater than seven-fold increase in sensitization risk 14 . Evidence suggests the microbiota may play a significant role in the development of GDM 9 , 15 , 16 . The gut microbiota, a highly intricate community of microorganisms such as bacteria, viruses, and fungi, plays a vital role in regulating various physiological functions 17 , 18 . While the term “second brain” was initially used to describe the enteric nervous system due to its rich neural network and functional independence, it is now increasingly applied to the gut microbiota. That occurs due to the influence of the microbiota on brain activity through the gut-brain axis, particularly through the production of neurotransmitters and immune-signaling molecules like cytokines 19 . Early-life disruptions in microbial composition have been associated with inflammatory, allergic, and metabolic immune-related conditions later in life 20 . Maternal health plays a crucial role in shaping the infant’s gut microbiota. Pregnancy and the postpartum period are characterized by notable microbial shifts, particularly in the gastrointestinal tract, oral cavity, and vaginal microbiota 21 , 22 . Interestingly, dysbiosis is not limited to the mother. Infants born to mothers with GDM also exhibit microbial imbalances, especially in their oral and gut microbiota 23 . In addition, children born to women with GDM show distinct microbiome patterns at birth, 2 weeks, and even 5 years postpartum, showing potential maternal microbial influence 24 – 27 . Furthermore, evidence shows that microbiota may influence metabolism and contribute to weight gain, obesity, preeclampsia, insulin sensitivity, and diabetes 28 – 32 . The initial process of gut colonization in children is typically dominated by Bifidobacterium , which gradually diversifies by the age of three 27 . This early phase is critical for immune development and defence against pathogens 9 , 33 . Factors such as gestational age, delivery method, maternal health, and diet shape this microbial diversity 34 , 35 . During the first months of life, breast milk plays a particularly central role in the formation of microbiome by providing bacterial species ( Staphylococcus, Streptococcus, Serratia, Pseudomonas, Corynebacterium, Ralstonia, Propionibacterium, Finegoldia, Sphingomonas, Bifidobacterium ) to the infant as well as beneficial components such as human milk oligosaccharides (HMOs) that promote infant gut microbiota development 36 – 41 . Additional maternal factors, including pre-pregnancy BMI, weight gain during pregnancy, breastfeeding stage, antibiotic use, mode of birth, and pregnancy-related diseases, also appear to influence the breast milk microbiome 41 – 45 . Despite this theoretical background, studies that explore a potential linkage of GDM to the breast milk microbiome have yielded mixed results, underlining the importance of additional studies on this topic 9 , 42 , 46 , 47 . When it comes to the Greek population, relevant evidence is also scarce 48 . Thus, in this study, we explore the breast milk microbiota of a well-defined group of 50 breastfeeding women, intending to compare the microbial profile between women with and without GDM. Results A comparison between the microbiota of breast milk from women with and without gestational diabetes is presented in Fig. 1 . This data represents the average number of reads from NGS of 25 samples for each type of breast milk (from women with GDM or those without). We found significant differences in the microbial abundances in the 2 studied groups, except for Prevotella veroralis , Staphylococcus hominis , Streptococcus mitis , and Streptococcus salivarius . Breastmilk in GDM samples contained significantly less Bacillus tropicus , Bacillus wiedmannii , Latilactobacillus graminis and Lacticaseibacillus paracasei . Even more, it was observed to contain significantly higher microbial loads of Acinetobacter johnsonii , Bacillus cereus , Bradyrhizobium mercantei , Gemella haemolysans , and Novospingobium pokkalii . When considering the taxonomy of these species, specific differences are noticed at the phylum level between samples of women with and without gestational diabetes. Three bacterial species that were found in both women with and without gestational diabetes belonged to the Bacillota (previously known as Firmicutes ), while the fourth species belonged to the Bacteroidota . The breast milk of women without GDM was significantly colonised by four other bacterial species, all belonging to the Bacillota . For women without GDM, on the other hand, two additional species of Bacillota were found in combination with three species of the Pseudomonadota (previously known as Proteobacteria ). As such, whereas the microbiota of breast milk of women without GDM almost exclusively consisted of Bacillota , breast milk of women with GDM contained contaminations of Pseudomonadota . To further illustrate these microbiota differences at the phylum level, the relative abundance of each phylum was calculated over the 25 samples of each group, i.e., with or without GDM. These relative abundances have been visualised in Fig. 2 . It is demonstrated that the microbiota of breast milk from women without gestational diabetes consists almost exclusively of Bacillota and Bacteroidota . In comparison, the microbiota of breast milk from women with gestational diabetes consists for over one quarter of Pseudomonadota . In relative terms, this increased level in Pseudomonadota coincides with an almost equal decrease in Bacillota . Discussion It has been established that the breast milk microbiota strongly influences the gastrointestinal colonisation in the infant 50 . As such, increased levels of Pseudomonadota in breast milk led to increased Pseudomonadota in the breastfed infant’s gut microbiota. Pseudomonadota have a low abundance in the gut of healthy humans and have been strongly correlated with human gut dysbiosis 51 . The link between cases of GDM and the occurrence of Pseudomonadota in gut microbiota has previously been described. Wu et al. (2022) investigated the effect of GDM on the human gut microbiota through faecal samples 52 . Along with other effects of GDM, they found that seven genera of the Pseudomonadota were increased significantly in participants with GDM compared to the microbiota of healthy participants. Moreover, Gravdal et al. (2023) found that an increase in genera belonging to the Pseudomonadota in the gut microbiota is a key indicator of prediabetes patients 53 . As such, the current study demonstrates that the metabolic changes associated with GDM might not only lead to increased Pseudomonadota in the gut microbiota but also breast milk microbiota. Research on the effect of GDM on breast milk microbiota is, however, inconclusive. Both LeMay-Nedjelski et al. (2020) and Rold et al. (2024) found no significant differences between the breast milk microbiota of women with GDM and healthy women 9 , 46 . In contrast, Gámez-Valdez et al. (2021) found that GDM led to a higher microbial diversity in human colostrum milk compared to the healthy participants 42 . Among other differences, they specifically found the proportion of Rhodobacteraceae , a family of the Pseudomonadota , to be in higher abundance in the breast milk from women with GDM. This result is in line with the findings of the current study. Table 1 Studies on breast milk microbiota in in the context of maternal GDM from different ethnic groups. Study Location Method Population Details Key Findings Reference Greece (Current Study) 16s rRNA gene (Full-length) Nanopore n = 50 women (25 with GDM,25 non- GDM) Were significant differences in the microbial composition between the two groups Current Study (2025) Denmark 16s rRNA gene (v4 region) Illumina n = 45 women (18 with GDM, 27 non- GDM) No statistically significant association found. Rold et al., 2024 Colombia 16s rRNA gene (V3-V4 region) Illumina Miseq n = 20 individuals 10 mother-infant pairs (5 GDM and 5 non-GDM) We found significant differences in the relative abundances of gut bacteria between two groups. Valencia-Castillo et al., 2025 Fuqing China 16s rRNA gene (V3-V4 region) Illumina Miseq n = 46 women 21 women with GDM, 25 non- GDM Were significant differences in the microbial composition between the two groups (p = 0.01) Zhu, H et al., 2022 Qingdao China 16s rRNA gene Illumina novaseq6000 n = 80 women 40 women with GDM, 40 non- GDM Significant differences in gut microbiota composition were found between infants of GDM and non- GDM. Lia, K et al., 2025 Table 1 shows comparative studies in Denmark, Colombia, and two regions of China (Fuqing and Qingdao) that revealed both shared and divergent patterns of breast milk microbiota and infant gut colonization in the context of maternal GDM. Specifically, in a Danish cohort sampled at 1–3 weeks postpartum, Streptococcus and Staphylococcus dominated breast milk from both GDM and non-GDM mothers, and no statistically significant differences in overall diversity or specific bacterial taxa were detected between groups. By contrast, in the Chinese Fuqing study (at ~ 42 days postpartum) found that GDM was associated with clear compositional shifts in the milk microbiota: mothers with GDM had significantly lower proportions of phyla such as Bacteroidetes ( Bacteroidota ) and Cyanobacteria , and reduced abundances of several genera (including Ralstonia , Rhodococcus , Burkholderia-Caballeronia - Paraburkholderia , Acinetobacter , and Fluviicola ) compared to normoglycemic mothers, with only an unclassified Xanthobacteraceae lineage relatively enriched. In Colombia, integrated analysis of mother–infant pairs showed that GDM coincided with gut dysbiosis in mothers and linked changes in breast milk and infant microbiomes. Colombian women with GDM exhibited reduced gut levels of commensals (notably Bifidobacterium , Serratia and Sutterella ), and their milk likewise contained lower Serratia , Sutterella and Lactococcus . Correspondingly, infants of GDM mothers had diminished colonization by putative beneficial taxa (including Bifidobacterium, Lactobacillus, Streptococcus, Serratia, Sutterella and Veillonella ) relative to infants of healthy mothers, suggesting vertical transmission of dysbiotic signatures. Finally, the prospective Chinese Qingdao cohort study found that GDM-associated changes in human milk composition were linked to infant gut perturbations: exclusively breastfed infants of GDM mothers showed significant shifts in multiple bacterial phyla and genera (e.g. altered abundances of various proteobacterial and other lineages) compared to controls, and these microbiota differences were inversely correlated with the blunted rise in omega-3 polyunsaturated fatty acids (such as alpha-linolenic acid) observed in GDM mothers’ milk over time. According to Table 1 , in some populations (e.g., the Danish study), breast milk microbiota appears largely unaffected by GDM, whereas in others (e.g., Fujian and Colombian cohorts), maternal glucose intolerance is associated with distinct microbial and compositional shifts. A common pattern across most studies is the reduced abundance of beneficial commensals and a tendency toward infant gut dysbiosis in the context of GDM. These alterations in the mother–milk–infant axis may influence infant health by modifying early microbial exposures and the nutritional environment, potentially predisposing offspring to an altered gut microbiome with consequences for immune development and metabolic programming. Despite some variability in geographic origin, sequencing methodologies, and sample types, most studies report significant microbial differences between GDM and non-GDM groups. Notably, the current Greek study and reports from China (Fuqing and Qingdao) and Colombia observed marked shifts in microbial composition or relative abundance in women with GDM or their infants, using both full-length and region-specific 16S rRNA sequencing platforms (Nanopore, Illumina MiSeq, NovaSeq). Only the Danish study failed to find a statistically significant association, possibly due to population-specific or methodological factors, such as small sample size or regional microbial variability Collectively, the evidence supports the hypothesis that maternal metabolic status, particularly GDM, plays a key role in shaping neonatal microbial exposures, possibly beginning in utero or during early postnatal interactions. These microbiome changes may, in turn, influence immune maturation, metabolic programming, and long-term health outcomes. To further elucidate these associations, future studies should adopt longitudinal designs, increase geographic and ethnic diversity, and employ multi-omics approaches—including metagenomics, metabolomics, and immunophenotyping—to clarify underlying mechanisms. Our study has some limitations, including the small sample size and the relatively broad time window for sample collection. Maternal dietary habits may also contribute to microbiome changes, and it is well known that the breast milk microbiota may change during the lactation period, with colostrum exhibiting a different microbial profile compared to mature milk in subsequent days. Due to the limited size of our cohort, we were unable to assess the impact of these temporal differences on the milk microbiome. Our study also has several strengths. The women were instructed to collect breast milk samples by manually expressing milk from a breast that had not been nursed for at least three hours, and the first volume of milk was discarded to minimize skin contamination. Additionally, we analyzed the microbiota not only at the genus level but also at the species level, allowing for a more detailed characterization. Differences at lower taxonomic or functional levels may therefore have been identified, providing deeper insights into the microbial composition. Conclusions Our study indicates that the microbiota of human milk from women with gestational diabetes mellitus (GDM) differed from that of women without GDM. The extent to which the maternal gut microbiota, the breast milk microbiota, or both contribute to the colonization of the infant microbiome, and whether other factors in the infant gut environment predispose offspring of mothers with GDM to dysbiosis, remains unknown. Further research involving large prospective mother-infant cohorts is necessary to understand how these early microbiome alterations may interact with host factors to influence immune responses and the development of chronic diseases in offspring of mothers with GDM. Although subtle changes in the microbiome may become less dominant over time, early microbial succession patterns during the first year of life have been associated with susceptibility to immune-mediated diseases later in life. Understanding the mechanisms by which GDM affects the offspring’s microbiome offers potential for developing preventive strategies. Interventions such as improving maternal nutrition during pregnancy and the use of probiotics could help maintain a healthy microbiome in the offspring, but additional research is required. Materials and methods Human Milk Samples Fifty independent human breast milk samples were collected from 50 Greek women volunteers, comprising two groups: 25 samples from women diagnosed with gestational diabetes mellitus (GDM) and 25 samples from women without GDM. Participants were recruited from the maternity wards of Attikon University Hospital within 1 to 3 days postpartum. Inclusion criteria were women with a gestational age at delivery between 37 + 0 and 41 + 6 weeks who delivered a healthy newborn. Αll the women had vaginal delivery. Exclusion criteria included a prior diagnosis of type 1 or type 2 diabetes, preeclampsia, chorioamnionitis, prelabor rupture of membranes, or prolonged rupture of membranes. Additionally, women who had taken antibiotics within the last three months were excluded. The diagnosis of GDM was established according to the International Association of Diabetes and Pregnancy Study Groups (IADPSG) criteria using a one-step 2-hour 75 g oral glucose tolerance test (OGTT). Gestational diabetes was diagnosed if any of the following plasma glucose values were exceeded: fasting glucose > 92 mg/dL, 1-hour glucose > 180 mg/dL, or 2-hour glucose > 153 mg/dL 8 , 49 . All the milk samples were collected within a time frame between 09:00 and 10:00 a.m., 5–10 days after the delivery. All the milk samples were collected in approximately one aliquot of 25 mL, and the presence of microbiota profiling was analysed. Before sample collection, mothers were given written instructions for standardization purposes. After washing their hands with soap and cleaning their nipples to minimize milk contamination, they were asked to use a BM pump with an automatic regulator to suction milk from the breast opposite to that from which their babies had previously suckled. Bottles and suction funnels were autoclaved before their use. The first volume of milk was discarded to minimize skin contamination. All samples were collected in bottles, immediately aliquoted under sterile conditions, transported in sterile tubes, and stored at − 20°C immediately after sampling. The research was approved by the Scientific Advisory Board of the Attikon Hospital and complied with all rules of bioethics of the declaration of Helsinki (Νο 452/2021). Genomic DNA Extraction and Next Generation Sequencing The milk samples were centrifuged at 14,000× g for 15 min. DNA was directly extracted from the cell pellet using the QIAamp® DNA kit (Qiagen, Hilden, Germany) following the protocol recommended by the supplier. The concentration of each concentrated was confirmed with an Invitrogen Qubit 4 fluorometer (Thermo Fisher Scientific, Waltham, MA, USA), 16S rRNA gene amplicon barcoding PCR and library preparation (ONT MinION 16S V1–V9 library preparation). The extracted gDNA was then prepared for prokaryotic metagenome sequencing using the 16S Barcoding Kit and 16S Barcoding Kit 0–24 (SQK-RAB204 and SQK-16S024, Oxford Nanopore Technologies, Oxford, UK), according to the manufacturer’s protocol, using 10 ng of the extracted gDNA per sample. The PCR reaction was performed on the full 16S hypervariable region (V1-V9) by injecting each multiplexing barcode included in the 16S Barcoding Kit 0–24 into 10 ng of each extracted DNA under the following conditions: initial 30 s denaturation at 98°C (Stage 1), 25 cycles of 10 s denaturation at 98°C, 30 s annealing at 55°C, 90 s extension at 65°C (Stage 2), and 5 min final extension at 65°C (Stage 3), with NEBNext® Ultra™ II Q5® Master Mix (New England Biolabs, Ipswich, MA, USA) as the PCR polymerase reagent mixture. The 16S V1–V9 amplicons were subsequently purified using Agencourt AMPure XP (Beckman Coulter, Brea, CA, USA) magnetic beads with a PCR reaction mix to magnetic bead ratio of 5:3 and washed twice with freshly prepared 70% ethanol. The final elution of purified DNA was performed by adding 12 µL of 10 mM Tris–HCl pH 8.0 with 50 mM NaCl, incubating for 2 min at room temperature, and recovering 10 µL of the elute from each tube. The concentration of each purified 16S V1–V9 amplicon was confirmed with an Invitrogen Qubit 4 fluorometer (Thermo Fisher Scientific, Waltham, MA, USA), and the samples were pooled with a total concentration of 100 fmol in 10 µL of the final library. Bioinformatics and statistical analysis The reliability of the subsampled read numbers was verified. Prior to subjecting the full dataset to taxonomic analysis, three random samples from the ONT sequencing were subsampled to 30,000 reads and 100,000 reads per sample to ensure adequate read depth was achieved. The results showed negligible differences of less than 0.1% in all taxonomic levels, showing that the read depth of 30,000 reads per sample was sufficient for detecting minor constituents of the 16S metagenome. For the main taxonomic analysis, 50,000 reads per sample were used to further ensure adequate read depth. For diversity analysis, the alpha rarefaction curves of various alpha diversity parameters were checked to verify the plateau of the curves. A one-way analysis of variance (ANOVA) was used to determine significant differences between the means of the number of NGS reads for breastmilk of women with and without gestational diabetes. This ANOVA was executed separately for each species, and the means were considered significantly different if p < 0.05. The ANOVA was implemented through the anovan -function of MATLAB R2024b (The MathWorks). Declarations Author Contributions: Conceptualization, D.H.; Investigation, E.P. D.V., S.L. M.T., P.H. E.R, M.P., K.L Z.S., N.V., A.G.T; Data Curation, S.A., E.T, J.V.I; Writing – Original Draft Preparation, S.L. D.V. S.A.; Writing – Review & Editing, E.T., D.V., M.T., P.H. E.R, M.P., K.L Z.S., N.V., A.G.T, D.H. J.V.I; Supervision, D.H. Acknowledgements: This work was supported by the European Commission within the framework of the Erasmus+ FOOD4S Programme (Erasmus Mundus Joint Master Degree in Food Systems Engineering, Technology and Business 619864-EPP-1-2020-1-BE-EPPKA1-JMD-MOB). Funding: Institutional Review Board Statement: This study was conducted according to the guidelines of the Declaration of Helsinki, and it was approved by the ethics committee of University General Hospital "ATTIKON" Ethical Committee with the protocol number 1235 (15 April 2020). Informed Consent Statement : Written informed consent has been obtained from the patients to participate in this study. 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Examining the relationship between maternal body size, gestational glucose tolerance status, mode of delivery and ethnicity on human milk microbiota at three months post-partum. BMC Microbiol. 20 , 219 (2020). Hughes, S. A., Perrella, S. L., Ireland, D. J., Geddes, D. T. & Stinson, L. F. Human Milk Microbiome Is Altered in Mothers with Gestational Diabetes Mellitus. in Australian Breastfeeding + Lactation Research and Science Translation Conference 2 (MDPI, Basel Switzerland, 2023). 2 (MDPI, Basel Switzerland, 2023). (2023). 10.3390/proceedings2023093002 Sokou, R. et al. The Impact of Gestational Diabetes Mellitus (GDM) on the Development and Composition of the Neonatal Gut Microbiota: A Systematic Review. Microorganisms 12 , 1564 (2024). Sweeting, A., Wong, J., Murphy, H. R. & Ross, G. P. A Clinical Update on Gestational Diabetes Mellitus. Endocr. Rev. 43 , 763–793 (2022). Notarbartolo, V., Giuffrè, M., Montante, C., Corsello, G. & Carta, M. Composition of Human Breast Milk Microbiota and Its Role in Children’s Health. Pediatr. Gastroenterol. Hepatol. Nutr. 25 , 194 (2022). Shin, N. R., Whon, T. W. & Bae, J. W. Proteobacteria: microbial signature of dysbiosis in gut microbiota. Trends Biotechnol. 33 , 496–503 (2015). Wu, N. et al. The Gut Microbial Signature of Gestational Diabetes Mellitus and the Association With Diet Intervention. Front Cell. Infect. Microbiol 11 , (2022). Gravdal, K. et al. Exploring the gut microbiota in patients with pre-diabetes and treatment naïve diabetes type 2 - a pilot study. BMC Endocr. Disord . 23 , 179 (2023). Additional Declarations No competing interests reported. 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University of Athens, \"ATTIKON\" University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Panagiotis","middleName":"","lastName":"Halvatsiotis","suffix":""}],"badges":[],"createdAt":"2025-07-31 13:08:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7262551/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7262551/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91931352,"identity":"467abdb4-a7ca-4a19-a127-7e78c568e857","added_by":"auto","created_at":"2025-09-23 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02:38:01","extension":"html","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":125474,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7262551/v1/7c1726a37b783806b848e576.html"},{"id":91932877,"identity":"161f2cbd-278d-49bb-9451-0412810cc9aa","added_by":"auto","created_at":"2025-09-23 02:38:00","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":70701,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of gestational diabetes on bacterial species in breast milk. Bars represent the average number of reads from next-generation sequencing based on 12 breastmilk samples from women with or without gestational diabetes. Error bars represent the standard deviation of the mean. Species names indicated with an asterisk (*) have a mean number of reads that is significantly influenced by the occurrence of gestational diabetes (p \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7262551/v1/431b0019b2491b4bf8e8b6d3.png"},{"id":91931343,"identity":"00697876-92b5-44f3-b282-b53f5f05a256","added_by":"auto","created_at":"2025-09-23 02:30:00","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":30441,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of gestational diabetes on the relative abundance of bacterial phyla in breast milk. Relative abundances were calculated from the sum of all next generation sequencing (NGS) reads per breast milk category. Phyla with less than 100 NGS reads in total were not considered.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7262551/v1/75ad97c47d69c275852f30df.png"},{"id":91935526,"identity":"53043aec-b67f-4aac-9edc-8aade9ca2052","added_by":"auto","created_at":"2025-09-23 02:46:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":746862,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7262551/v1/7453098d-f8ea-4d6b-88c4-2724e3e8ac19.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Microbiota Profiling of Breast Milk in Relation to Gestational Diabetes Mellitus Status - A Greek pilot study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGestational Diabetes Mellitus (GDM) is a metabolic disorder arising during pregnancy in women without a history of diabetes and typically resolving following delivery \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. It is the most common metabolic complication of gestation, affecting even up the 25% of pregnancies globally, with an incidence depending on the studied population and the diagnostic criteria applied \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. According to the International Diabetes Federation report, the global incidence of GDM for certain European populations ranges from 1\u0026ndash;14%, while its prevalence may be as high as 20\u0026ndash;25% \u003csup\u003e4,5\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eGDM is primarily characterized by increased insulin resistance status during the second and third trimesters of pregnancy. driven hormonally by the increased levels of the human placental lactogen (hPL), cortisol, estrogen, and progesterone in circulation. Insensitivity to insulin gradually prompts pancreatic β-cells to fail in compensating for the higher insulin demands and consequently maternal hyperglycemia ensues \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Key risk factors for GDM include maternal obesity, maternal age\u0026thinsp;\u0026gt;\u0026thinsp;25 years, family history of diabetes, prediabetes, polycystic ovary syndrome (PCOS), a previous diagnosis of GDM, and Asian, African American, or Hispanic ethnic background, among others \u003csup\u003e\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAccording to findings from the Hyperglycemia and Adverse Pregnancy Outcome (HAPO) study, maternal hyperglycemia is a key factor contributing to multiple metabolic disturbances in both the mother and fetus \u003csup\u003e\u003cspan additionalcitationids=\"CR11 CR12\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. GDM has been associated with many adverse maternal and neonatal outcomes, including cesarean delivery, preeclampsia, preterm birth, large-for-gestational-age (LGA) neonates, shoulder dystocia, and neonatal hypoglycemia. Women diagnosed with GDM and their offspring face an increased long-term risk of developing type 2 diabetes mellitus (T2DM), obesity, cardiovascular disease, and other metabolic disorders.\u003c/p\u003e\u003cp\u003eEmerging evidence also indicates that neonates of mothers with GDM are at elevated risk for immune-mediated conditions, such as atopic dermatitis and allergen sensitization. A clinical study reported that infants born to mothers with GDM exhibited more than a five-fold increase in the risk of allergen sensitization. Furthermore, these infants were more likely to develop atopic dermatitis, which itself is associated with a greater than seven-fold increase in sensitization risk \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Evidence suggests the microbiota may play a significant role in the development of GDM \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. The gut microbiota, a highly intricate community of microorganisms such as bacteria, viruses, and fungi, plays a vital role in regulating various physiological functions \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. While the term \u0026ldquo;second brain\u0026rdquo; was initially used to describe the enteric nervous system due to its rich neural network and functional independence, it is now increasingly applied to the gut microbiota. That occurs due to the influence of the microbiota on brain activity through the gut-brain axis, particularly through the production of neurotransmitters and immune-signaling molecules like cytokines\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Early-life disruptions in microbial composition have been associated with inflammatory, allergic, and metabolic immune-related conditions later in life \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Maternal health plays a crucial role in shaping the infant\u0026rsquo;s gut microbiota. Pregnancy and the postpartum period are characterized by notable microbial shifts, particularly in the gastrointestinal tract, oral cavity, and vaginal microbiota \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Interestingly, dysbiosis is not limited to the mother. Infants born to mothers with GDM also exhibit microbial imbalances, especially in their oral and gut microbiota \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. In addition, children born to women with GDM show distinct microbiome patterns at birth, 2 weeks, and even 5 years postpartum, showing potential maternal microbial influence \u003csup\u003e\u003cspan additionalcitationids=\"CR25 CR26\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Furthermore, evidence shows that microbiota may influence metabolism and contribute to weight gain, obesity, preeclampsia, insulin sensitivity, and diabetes \u003csup\u003e\u003cspan additionalcitationids=\"CR29 CR30 CR31\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. The initial process of gut colonization in children is typically dominated by \u003cem\u003eBifidobacterium\u003c/em\u003e, which gradually diversifies by the age of three \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. This early phase is critical for immune development and defence against pathogens \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Factors such as gestational age, delivery method, maternal health, and diet shape this microbial diversity \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. During the first months of life, breast milk plays a particularly central role in the formation of microbiome by providing bacterial species (\u003cem\u003eStaphylococcus, Streptococcus, Serratia, Pseudomonas, Corynebacterium, Ralstonia, Propionibacterium, Finegoldia, Sphingomonas, Bifidobacterium\u003c/em\u003e) to the infant as well as beneficial components such as human milk oligosaccharides (HMOs) that promote infant gut microbiota development \u003csup\u003e\u003cspan additionalcitationids=\"CR37 CR38 CR39 CR40\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Additional maternal factors, including pre-pregnancy BMI, weight gain during pregnancy, breastfeeding stage, antibiotic use, mode of birth, and pregnancy-related diseases, also appear to influence the breast milk microbiome \u003csup\u003e\u003cspan additionalcitationids=\"CR42 CR43 CR44\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. Despite this theoretical background, studies that explore a potential linkage of GDM to the breast milk microbiome have yielded mixed results, underlining the importance of additional studies on this topic \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e,\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e,\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. When it comes to the Greek population, relevant evidence is also scarce \u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. Thus, in this study, we explore the breast milk microbiota of a well-defined group of 50 breastfeeding women, intending to compare the microbial profile between women with and without GDM.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eA comparison between the microbiota of breast milk from women with and without gestational diabetes is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. This data represents the average number of reads from NGS of 25 samples for each type of breast milk (from women with GDM or those without).\u003c/p\u003e\u003cp\u003eWe found significant differences in the microbial abundances in the 2 studied groups, except for \u003cem\u003ePrevotella veroralis\u003c/em\u003e, \u003cem\u003eStaphylococcus hominis\u003c/em\u003e, \u003cem\u003eStreptococcus mitis\u003c/em\u003e, and \u003cem\u003eStreptococcus salivarius\u003c/em\u003e. Breastmilk in GDM samples contained significantly less \u003cem\u003eBacillus tropicus\u003c/em\u003e, \u003cem\u003eBacillus wiedmannii\u003c/em\u003e, \u003cem\u003eLatilactobacillus graminis\u003c/em\u003e and \u003cem\u003eLacticaseibacillus paracasei\u003c/em\u003e. Even more, it was observed to contain significantly higher microbial loads of \u003cem\u003eAcinetobacter johnsonii\u003c/em\u003e, \u003cem\u003eBacillus cereus\u003c/em\u003e, \u003cem\u003eBradyrhizobium mercantei\u003c/em\u003e, \u003cem\u003eGemella haemolysans\u003c/em\u003e, and \u003cem\u003eNovospingobium pokkalii\u003c/em\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWhen considering the taxonomy of these species, specific differences are noticed at the phylum level between samples of women with and without gestational diabetes. Three bacterial species that were found in both women with and without gestational diabetes belonged to the \u003cem\u003eBacillota\u003c/em\u003e (previously known as \u003cem\u003eFirmicutes\u003c/em\u003e), while the fourth species belonged to the \u003cem\u003eBacteroidota\u003c/em\u003e. The breast milk of women without GDM was significantly colonised by four other bacterial species, all belonging to the \u003cem\u003eBacillota\u003c/em\u003e. For women without GDM, on the other hand, two additional species of Bacillota were found in combination with three species of the \u003cem\u003ePseudomonadota\u003c/em\u003e (previously known as \u003cem\u003eProteobacteria\u003c/em\u003e). As such, whereas the microbiota of breast milk of women without GDM almost exclusively consisted of \u003cem\u003eBacillota\u003c/em\u003e, breast milk of women with GDM contained contaminations of \u003cem\u003ePseudomonadota\u003c/em\u003e.\u003c/p\u003e\u003cp\u003e To further illustrate these microbiota differences at the phylum level, the relative abundance of each phylum was calculated over the 25 samples of each group, i.e., with or without GDM. These relative abundances have been visualised in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. It is demonstrated that the microbiota of breast milk from women without gestational diabetes consists almost exclusively of \u003cem\u003eBacillota\u003c/em\u003e and \u003cem\u003eBacteroidota\u003c/em\u003e. In comparison, the microbiota of breast milk from women with gestational diabetes consists for over one quarter of \u003cem\u003ePseudomonadota\u003c/em\u003e. In relative terms, this increased level in \u003cem\u003ePseudomonadota\u003c/em\u003e coincides with an almost equal decrease in \u003cem\u003eBacillota\u003c/em\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIt has been established that the breast milk microbiota strongly influences the gastrointestinal colonisation in the infant\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. As such, increased levels of \u003cem\u003ePseudomonadota\u003c/em\u003e in breast milk led to increased \u003cem\u003ePseudomonadota\u003c/em\u003e in the breastfed infant\u0026rsquo;s gut microbiota. \u003cem\u003ePseudomonadota\u003c/em\u003e have a low abundance in the gut of healthy humans and have been strongly correlated with human gut dysbiosis\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe link between cases of GDM and the occurrence of \u003cem\u003ePseudomonadota\u003c/em\u003e in gut microbiota has previously been described. Wu et al. (2022) investigated the effect of GDM on the human gut microbiota through faecal samples \u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. Along with other effects of GDM, they found that seven genera of the \u003cem\u003ePseudomonadota\u003c/em\u003e were increased significantly in participants with GDM compared to the microbiota of healthy participants. Moreover, Gravdal et al. (2023) found that an increase in genera belonging to the \u003cem\u003ePseudomonadota\u003c/em\u003e in the gut microbiota is a key indicator of prediabetes patients \u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. As such, the current study demonstrates that the metabolic changes associated with GDM might not only lead to increased \u003cem\u003ePseudomonadota\u003c/em\u003e in the gut microbiota but also breast milk microbiota.\u003c/p\u003e\u003cp\u003eResearch on the effect of GDM on breast milk microbiota is, however, inconclusive. Both LeMay-Nedjelski et al. (2020) and Rold et al. (2024) found no significant differences between the breast milk microbiota of women with GDM and healthy women \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. In contrast, G\u0026aacute;mez-Valdez et al. (2021) found that GDM led to a higher microbial diversity in human colostrum milk compared to the healthy participants \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. Among other differences, they specifically found the proportion of \u003cem\u003eRhodobacteraceae\u003c/em\u003e, a family of the \u003cem\u003ePseudomonadota\u003c/em\u003e, to be in higher abundance in the breast milk from women with GDM. This result is in line with the findings of the current study.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eStudies on breast milk microbiota in in the context of maternal GDM from different ethnic groups.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStudy Location\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMethod\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePopulation Details\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eKey Findings\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGreece\u003c/p\u003e\u003cp\u003e(Current Study)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16s rRNA gene\u003c/p\u003e\u003cp\u003e(Full-length)\u003c/p\u003e\u003cp\u003eNanopore\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003en\u0026thinsp;=\u0026thinsp;50 women\u003c/p\u003e\u003cp\u003e(25 with GDM,25 non- GDM)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWere significant differences in the microbial composition between the two groups\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCurrent Study (2025)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDenmark\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16s rRNA gene\u003c/p\u003e\u003cp\u003e(v4 region)\u003c/p\u003e\u003cp\u003eIllumina\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003en\u0026thinsp;=\u0026thinsp;45 women\u003c/p\u003e\u003cp\u003e(18 with GDM, 27 non- GDM)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNo statistically significant association found.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRold et al., 2024\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eColombia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16s rRNA gene\u003c/p\u003e\u003cp\u003e(V3-V4 region)\u003c/p\u003e\u003cp\u003eIllumina Miseq\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003en\u0026thinsp;=\u0026thinsp;20 individuals\u003c/p\u003e\u003cp\u003e10 mother-infant pairs\u003c/p\u003e\u003cp\u003e(5 GDM and 5 non-GDM)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWe found significant differences in the relative abundances of gut bacteria between two groups.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eValencia-Castillo et al., 2025\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFuqing\u003c/p\u003e\u003cp\u003eChina\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16s rRNA gene\u003c/p\u003e\u003cp\u003e(V3-V4 region)\u003c/p\u003e\u003cp\u003eIllumina Miseq\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003en\u0026thinsp;=\u0026thinsp;46 women\u003c/p\u003e\u003cp\u003e21 women with GDM, 25 non- GDM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWere significant differences in the microbial composition between the two groups (p\u0026thinsp;=\u0026thinsp;0.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eZhu, H et al., 2022\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQingdao China\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16s rRNA gene\u003c/p\u003e\u003cp\u003eIllumina novaseq6000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003en\u0026thinsp;=\u0026thinsp;80 women\u003c/p\u003e\u003cp\u003e40 women with GDM, 40 non- GDM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSignificant differences in gut microbiota composition were found between infants of GDM and non- GDM.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLia, K et al., 2025\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows comparative studies in Denmark, Colombia, and two regions of China (Fuqing and Qingdao) that revealed both shared and divergent patterns of breast milk microbiota and infant gut colonization in the context of maternal GDM. Specifically, in a Danish cohort sampled at 1\u0026ndash;3 weeks postpartum, \u003cem\u003eStreptococcus\u003c/em\u003e and \u003cem\u003eStaphylococcus\u003c/em\u003e dominated breast milk from both GDM and non-GDM mothers, and no statistically significant differences in overall diversity or specific bacterial taxa were detected between groups.\u003c/p\u003e\u003cp\u003eBy contrast, in the Chinese Fuqing study (at ~\u0026thinsp;42 days postpartum) found that GDM was associated with clear compositional shifts in the milk microbiota: mothers with GDM had significantly lower proportions of phyla such as \u003cem\u003eBacteroidetes\u003c/em\u003e (\u003cem\u003eBacteroidota\u003c/em\u003e) and \u003cem\u003eCyanobacteria\u003c/em\u003e, and reduced abundances of several genera (including \u003cem\u003eRalstonia\u003c/em\u003e, \u003cem\u003eRhodococcus\u003c/em\u003e, \u003cem\u003eBurkholderia-Caballeronia\u003c/em\u003e-\u003cem\u003eParaburkholderia\u003c/em\u003e, \u003cem\u003eAcinetobacter\u003c/em\u003e, and \u003cem\u003eFluviicola\u003c/em\u003e) compared to normoglycemic mothers, with only an unclassified \u003cem\u003eXanthobacteraceae\u003c/em\u003e lineage relatively enriched.\u003c/p\u003e\u003cp\u003eIn Colombia, integrated analysis of mother\u0026ndash;infant pairs showed that GDM coincided with gut dysbiosis in mothers and linked changes in breast milk and infant microbiomes. Colombian women with GDM exhibited reduced gut levels of commensals (notably \u003cem\u003eBifidobacterium\u003c/em\u003e, \u003cem\u003eSerratia\u003c/em\u003e and \u003cem\u003eSutterella\u003c/em\u003e), and their milk likewise contained lower \u003cem\u003eSerratia\u003c/em\u003e, \u003cem\u003eSutterella\u003c/em\u003e and \u003cem\u003eLactococcus\u003c/em\u003e. Correspondingly, infants of GDM mothers had diminished colonization by putative beneficial taxa (including \u003cem\u003eBifidobacterium, Lactobacillus, Streptococcus, Serratia, Sutterella\u003c/em\u003e and \u003cem\u003eVeillonella\u003c/em\u003e) relative to infants of healthy mothers, suggesting vertical transmission of dysbiotic signatures.\u003c/p\u003e\u003cp\u003eFinally, the prospective Chinese Qingdao cohort study found that GDM-associated changes in human milk composition were linked to infant gut perturbations: exclusively breastfed infants of GDM mothers showed significant shifts in multiple bacterial phyla and genera (e.g. altered abundances of various proteobacterial and other lineages) compared to controls, and these microbiota differences were inversely correlated with the blunted rise in omega-3 polyunsaturated fatty acids (such as alpha-linolenic acid) observed in GDM mothers\u0026rsquo; milk over time.\u003c/p\u003e\u003cp\u003eAccording to Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, in some populations (e.g., the Danish study), breast milk microbiota appears largely unaffected by GDM, whereas in others (e.g., Fujian and Colombian cohorts), maternal glucose intolerance is associated with distinct microbial and compositional shifts. A common pattern across most studies is the reduced abundance of beneficial commensals and a tendency toward infant gut dysbiosis in the context of GDM. These alterations in the mother\u0026ndash;milk\u0026ndash;infant axis may influence infant health by modifying early microbial exposures and the nutritional environment, potentially predisposing offspring to an altered gut microbiome with consequences for immune development and metabolic programming.\u003c/p\u003e\u003cp\u003eDespite some variability in geographic origin, sequencing methodologies, and sample types, most studies report significant microbial differences between GDM and non-GDM groups. Notably, the current Greek study and reports from China (Fuqing and Qingdao) and Colombia observed marked shifts in microbial composition or relative abundance in women with GDM or their infants, using both full-length and region-specific 16S rRNA sequencing platforms (Nanopore, Illumina MiSeq, NovaSeq). Only the Danish study failed to find a statistically significant association, possibly due to population-specific or methodological factors, such as small sample size or regional microbial variability\u003c/p\u003e\u003cp\u003eCollectively, the evidence supports the hypothesis that maternal metabolic status, particularly GDM, plays a key role in shaping neonatal microbial exposures, possibly beginning in utero or during early postnatal interactions. These microbiome changes may, in turn, influence immune maturation, metabolic programming, and long-term health outcomes. To further elucidate these associations, future studies should adopt longitudinal designs, increase geographic and ethnic diversity, and employ multi-omics approaches\u0026mdash;including metagenomics, metabolomics, and immunophenotyping\u0026mdash;to clarify underlying mechanisms. Our study has some limitations, including the small sample size and the relatively broad time window for sample collection. Maternal dietary habits may also contribute to microbiome changes, and it is well known that the breast milk microbiota may change during the lactation period, with colostrum exhibiting a different microbial profile compared to mature milk in subsequent days. Due to the limited size of our cohort, we were unable to assess the impact of these temporal differences on the milk microbiome. Our study also has several strengths. The women were instructed to collect breast milk samples by manually expressing milk from a breast that had not been nursed for at least three hours, and the first volume of milk was discarded to minimize skin contamination. Additionally, we analyzed the microbiota not only at the genus level but also at the species level, allowing for a more detailed characterization. Differences at lower taxonomic or functional levels may therefore have been identified, providing deeper insights into the microbial composition.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOur study indicates that the microbiota of human milk from women with gestational diabetes mellitus (GDM) differed from that of women without GDM. The extent to which the maternal gut microbiota, the breast milk microbiota, or both contribute to the colonization of the infant microbiome, and whether other factors in the infant gut environment predispose offspring of mothers with GDM to dysbiosis, remains unknown. Further research involving large prospective mother-infant cohorts is necessary to understand how these early microbiome alterations may interact with host factors to influence immune responses and the development of chronic diseases in offspring of mothers with GDM. Although subtle changes in the microbiome may become less dominant over time, early microbial succession patterns during the first year of life have been associated with susceptibility to immune-mediated diseases later in life. Understanding the mechanisms by which GDM affects the offspring\u0026rsquo;s microbiome offers potential for developing preventive strategies. Interventions such as improving maternal nutrition during pregnancy and the use of probiotics could help maintain a healthy microbiome in the offspring, but additional research is required.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e\u003cem\u003eHuman Milk Samples\u003c/em\u003e\u003c/p\u003e\u003cp\u003eFifty independent human breast milk samples were collected from 50 Greek women volunteers, comprising two groups: 25 samples from women diagnosed with gestational diabetes mellitus (GDM) and 25 samples from women without GDM.\u003c/p\u003e\u003cp\u003eParticipants were recruited from the maternity wards of Attikon University Hospital within 1 to 3 days postpartum. Inclusion criteria were women with a gestational age at delivery between 37\u0026thinsp;+\u0026thinsp;0 and 41\u0026thinsp;+\u0026thinsp;6 weeks who delivered a healthy newborn. Αll the women had vaginal delivery. Exclusion criteria included a prior diagnosis of type 1 or type 2 diabetes, preeclampsia, chorioamnionitis, prelabor rupture of membranes, or prolonged rupture of membranes. Additionally, women who had taken antibiotics within the last three months were excluded.\u003c/p\u003e\u003cp\u003eThe diagnosis of GDM was established according to the International Association of Diabetes and Pregnancy Study Groups (IADPSG) criteria using a one-step 2-hour 75 g oral glucose tolerance test (OGTT). Gestational diabetes was diagnosed if any of the following plasma glucose values were exceeded: fasting glucose\u0026thinsp;\u0026gt;\u0026thinsp;92 mg/dL, 1-hour glucose\u0026thinsp;\u0026gt;\u0026thinsp;180 mg/dL, or 2-hour glucose\u0026thinsp;\u0026gt;\u0026thinsp;153 mg/dL \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAll the milk samples were collected within a time frame between 09:00 and 10:00 a.m., 5\u0026ndash;10 days after the delivery. All the milk samples were collected in approximately one aliquot of 25 mL, and the presence of microbiota profiling was analysed. Before sample collection, mothers were given written instructions for standardization purposes. After washing their hands with soap and cleaning their nipples to minimize milk contamination, they were asked to use a BM pump with an automatic regulator to suction milk from the breast opposite to that from which their babies had previously suckled. Bottles and suction funnels were autoclaved before their use. The first volume of milk was discarded to minimize skin contamination.\u003c/p\u003e\u003cp\u003eAll samples were collected in bottles, immediately aliquoted under sterile conditions, transported in sterile tubes, and stored at \u0026minus;\u0026thinsp;20\u0026deg;C immediately after sampling. The research was approved by the Scientific Advisory Board of the Attikon Hospital and complied with all rules of bioethics of the declaration of Helsinki (Νο 452/2021).\u003c/p\u003e\u003cp\u003e\u003cem\u003eGenomic DNA Extraction and Next Generation Sequencing\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThe milk samples were centrifuged at 14,000\u0026times; g for 15 min. DNA was directly extracted from the cell pellet using the QIAamp\u0026reg; DNA kit (Qiagen, Hilden, Germany) following the protocol recommended by the supplier. The concentration of each concentrated was confirmed with an Invitrogen Qubit 4 fluorometer (Thermo Fisher Scientific, Waltham, MA, USA), 16S rRNA gene amplicon barcoding PCR and library preparation (ONT MinION 16S V1\u0026ndash;V9 library preparation).\u003c/p\u003e\u003cp\u003eThe extracted gDNA was then prepared for prokaryotic metagenome sequencing using the 16S Barcoding Kit and 16S Barcoding Kit 0\u0026ndash;24 (SQK-RAB204 and SQK-16S024, Oxford Nanopore Technologies, Oxford, UK), according to the manufacturer\u0026rsquo;s protocol, using 10 ng of the extracted gDNA per sample. The PCR reaction was performed on the full 16S hypervariable region (V1-V9) by injecting each multiplexing barcode included in the 16S Barcoding Kit 0\u0026ndash;24 into 10 ng of each extracted DNA under the following conditions: initial 30 s denaturation at 98\u0026deg;C (Stage 1), 25 cycles of 10 s denaturation at 98\u0026deg;C, 30 s annealing at 55\u0026deg;C, 90 s extension at 65\u0026deg;C (Stage 2), and 5 min final extension at 65\u0026deg;C (Stage 3), with NEBNext\u0026reg; Ultra\u0026trade; II Q5\u0026reg; Master Mix (New England Biolabs, Ipswich, MA, USA) as the PCR polymerase reagent mixture. The 16S V1\u0026ndash;V9 amplicons were subsequently purified using Agencourt AMPure XP (Beckman Coulter, Brea, CA, USA) magnetic beads with a PCR reaction mix to magnetic bead ratio of 5:3 and washed twice with freshly prepared 70% ethanol. The final elution of purified DNA was performed by adding 12 \u0026micro;L of 10 mM Tris\u0026ndash;HCl pH 8.0 with 50 mM NaCl, incubating for 2 min at room temperature, and recovering 10 \u0026micro;L of the elute from each tube. The concentration of each purified 16S V1\u0026ndash;V9 amplicon was confirmed with an Invitrogen Qubit 4 fluorometer (Thermo Fisher Scientific, Waltham, MA, USA), and the samples were pooled with a total concentration of 100 fmol in 10 \u0026micro;L of the final library.\u003c/p\u003e\u003cp\u003e\u003cem\u003eBioinformatics and statistical analysis\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThe reliability of the subsampled read numbers was verified. Prior to subjecting the full dataset to taxonomic analysis, three random samples from the ONT sequencing were subsampled to 30,000 reads and 100,000 reads per sample to ensure adequate read depth was achieved. The results showed negligible differences of less than 0.1% in all taxonomic levels, showing that the read depth of 30,000 reads per sample was sufficient for detecting minor constituents of the 16S metagenome. For the main taxonomic analysis, 50,000 reads per sample were used to further ensure adequate read depth. For diversity analysis, the alpha rarefaction curves of various alpha diversity parameters were checked to verify the plateau of the curves.\u003c/p\u003e\u003cp\u003eA one-way analysis of variance (ANOVA) was used to determine significant differences between the means of the number of NGS reads for breastmilk of women with and without gestational diabetes. This ANOVA was executed separately for each species, and the means were considered significantly different if p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. The ANOVA was implemented through the \u003cem\u003eanovan\u003c/em\u003e-function of MATLAB R2024b (The MathWorks).\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e Conceptualization, D.H.; Investigation, E.P. D.V., S.L. M.T., P.H. E.R, M.P., K.L Z.S., N.V., A.G.T; Data Curation, S.A., E.T, J.V.I; \u0026nbsp;Writing – Original Draft Preparation, S.L. D.V. S.A.; Writing – Review \u0026amp; Editing, E.T., D.V., M.T., P.H. E.R, M.P., K.L Z.S., N.V., A.G.T, D.H. J.V.I; Supervision, D.H.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003eThis work was supported by the European Commission within the framework of the Erasmus+ FOOD4S Programme (Erasmus Mundus Joint Master Degree in Food Systems Engineering, Technology and Business 619864-EPP-1-2020-1-BE-EPPKA1-JMD-MOB).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInstitutional Review Board Statement:\u003c/strong\u003e This study was conducted according to the guidelines of the Declaration of Helsinki, and it was approved by the ethics committee of University General Hospital \"ATTIKON\" Ethical Committee with the protocol number 1235 (15 April 2020).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent Statement\u003c/strong\u003e: Written informed consent has been obtained from the patients to participate in this study. The consent form for participation was distributed to all participants and signed\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability :\u0026nbsp;\u003c/strong\u003eThe datasets analysed during the current study available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest:\u0026nbsp;\u003c/strong\u003eThe authors declare no conflict of interest\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWang, H. et al. IDF Diabetes Atlas: Estimation of Global and Regional Gestational Diabetes Mellitus Prevalence for 2021 by International Association of Diabetes in Pregnancy Study Group\u0026rsquo;s Criteria. \u003cem\u003eDiabetes Res. Clin. Pract.\u003c/em\u003e \u003cb\u003e183\u003c/b\u003e, 109050 (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHu, J. et al. 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Disord\u003c/em\u003e. \u003cb\u003e23\u003c/b\u003e, 179 (2023).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Gestational diabetes mellitus, microbiota, breast milk, microbiota profiing, NGS analysis","lastPublishedDoi":"10.21203/rs.3.rs-7262551/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7262551/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGestational diabetes mellitus (GDM) is associated with metabolic alterations that may influence maternal and neonatal microbiota. While the impact of GDM on the infant gut microbiome has been increasingly studied, its effect on the breast milk microbiota remains poorly understood. In this pilot study, we compared the breast milk microbiota received from 25 women with GDM compared to that of 25 non-diabetic breastfeeding mothers to serve as controls using full-length 16S rRNA gene sequencing on the Oxford Nanopore platform. Significant differences in microbial composition were observed between the two groups. Breast milk from GDM mothers showed increased abundance of \u003cem\u003ePseudomonadota\u003c/em\u003e (formerly \u003cem\u003eProteobacteria\u003c/em\u003e), including species such as \u003cem\u003eAcinetobacter johnsonii\u003c/em\u003e and \u003cem\u003eBradyrhizobium mercantei\u003c/em\u003e, while beneficial \u003cem\u003eBacillota\u003c/em\u003e (e.g., \u003cem\u003eLacticaseibacillus paracasei\u003c/em\u003e) were significantly reduced compared to non-GDM samples. These findings suggest that GDM may alter breast milk microbial composition in ways that could influence neonatal early-life microbial colonization and immune programming. Given the known links between dysbiosis and metabolic and immune-mediated diseases, our results underscore the need for longitudinal, multi-omic studies to elucidate the long-term health implications of GDM-associated shifts in the breast milk microbiome.\u003c/p\u003e","manuscriptTitle":"A Microbiota Profiling of Breast Milk in Relation to Gestational Diabetes Mellitus Status - A Greek pilot study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-23 02:29:56","doi":"10.21203/rs.3.rs-7262551/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-14T15:16:07+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-14T13:33:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"178932968951525526432791254694153766210","date":"2026-01-14T11:25:26+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-11T02:37:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"32342583787288375435931965969585015472","date":"2026-01-02T20:09:59+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-20T17:17:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"113783430697802782588145964668445926971","date":"2025-09-13T07:54:43+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-12T11:59:57+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-12T06:52:36+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-11T11:21:36+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-08-07T16:03:47+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-08-05T19:11:46+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cca519de-3f76-4bdf-9a1c-78e03684a31e","owner":[],"postedDate":"September 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":54661067,"name":"Health sciences/Diseases"},{"id":54661068,"name":"Biological sciences/Microbiology"}],"tags":[],"updatedAt":"2026-03-02T13:24:28+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-23 02:29:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7262551","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7262551","identity":"rs-7262551","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}