SEARCHCOHORTendocrinology, obstetrics

cohort·endocrinology, obstetrics, gastroenterology, immunology, maternal-fetal medicine·PMC10086485

Gestational diabetes is driven by microbiota-induced inflammation months before diagnosis

Gut · 33 authors, 19 centres

AI SUMMARY

FIDELITY 100%

POPULATION394 pregnant women aged 18–40 years recruited at gestational weeks 11+0–13+6 from women's health centers in Israel (primary cohort), plus a secondary cohort of 66 women with GDM recruited at 24–28 weeks

INTERVENTIONObservational — no intervention; profiling of gut microbiome (16S rRNA sequencing), serum cytokines, fecal metabolomics/SCFAs, and clinical data in first trimester

COMPARISONWomen who later developed GDM (n=44, 11%) vs. women who remained normoglycemic (n=350) in the primary cohort

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TL;DR

This prospective cohort study of 394 pregnant women found that gestational diabetes mellitus (GDM) is preceded by gut microbiome alterations, elevated proinflammatory cytokines (including IL-6), and reduced fecal short-chain fatty acids detectable in the first trimester, months before clinical diagnosis. Fecal microbiota transplant experiments in germ-free mice confirmed that first-trimester gut microbes from women who later developed GDM could transfer glucose intolerance and elevated IL-6 to recipient animals. A machine learning model combining microbiome, cytokine, and clinical data predicted GDM onset with high accuracy (auROC=0.83), suggesting that early intervention windows exist for prevention.

Full summary

4,474 CHARS

**Background:** Gestational diabetes mellitus (GDM) affects approximately 10% of pregnancies worldwide and is associated with maternal and neonatal complications including pre-eclampsia, type 2 diabetes, macrosomia, and hypoglycemia. Current diagnosis occurs in the second or third trimester via glucose tolerance testing. The gut microbiome has been implicated in diabetes pathogenesis, but its role in GDM before clinical diagnosis, and the potential for first-trimester prediction, remained unclear.

**Methods:** The authors prospectively enrolled 394 pregnant women (gestational weeks 11+0–13+6) from Clalit HMO in Israel (2016–2017). Exclusion criteria included pre-existing diabetes, IVF or hormonal therapy in prior 3 months, antibiotic use in prior 3 months, and multiple gestation. Women were retroactively classified as GDM (n=44, 11%) or control (n=350) based on second-trimester GTT results. First-trimester samples included: fecal samples for 16S rRNA gene sequencing (V4 region, Illumina MiSeq), fecal short-chain fatty acid (SCFA) profiling, untargeted fecal metabolomics, and serum cytokine/hormone panels. Dietary intake (24-hour recall), physical activity, stress questionnaires, and clinical data were collected. Fecal microbiota transplant (FMT) experiments were performed using germ-free female mice receiving first-trimester fecal samples from age/BMI-matched GDM-control pairs from the primary cohort and two additional independent cohorts (Finnish and American). Glucose tolerance tests (ipGTT) and serum cytokine measurements were performed in recipient mice. A machine learning model (Xgboost) was developed using 20%/80% test/training split with fivefold cross-validation, and validated on an independent Chinese cohort of 98 GDM-control pairs.

**Key Results:** Women who later developed GDM had significantly higher pre-pregnancy BMI (28.2±7.7 vs. 22.6±4.0, p<0.0001), older age (33.1±3.77 vs. 31.2±4.43 years, p<0.01), higher fasting glucose (89.30±14.1 vs. 82.46±7.08 mg/dL, p<0.0001), and higher smoking rates (11/39 vs. 32/312, p<0.01). Serum cytokine profiling (GDM n=35, control n=78) revealed significantly elevated levels of IL-4, IL-6, IL-8, GM-CSF, and TNF-α in the GDM group (p<0.05, FDR-corrected Mann-Whitney U tests), robust to BMI and age adjustment. Fecal SCFA analysis (20 age-matched pairs) showed significantly reduced isovalerate and isobutyrate in the GDM group (p<0.05), with a trend for valerate (p=0.09). Gut microbiome α-diversity did not differ between groups, but unweighted UniFrac distances trended toward significance (p=0.06, PERMANOVA). After controlling for BMI and age, 15 bacterial species were under-represented in the GDM group, including *Prevotella copri*. FMT experiments: mice receiving GDM-donor microbiota exhibited significantly different microbial communities (p=0.005, PERMANOVA), reduced *P. copri* (p=0.04), impaired glucose tolerance (p<0.05 at multiple time points), and elevated IL-6 and IL-10 (p<0.05). These findings were replicated across Finnish and American cohorts (combined p=0.022 at 30 min, Fisher's method). Fecal metabolomics (15 age/BMI-matched pairs) showed 50 of 52 differentially abundant short peptides were depleted in GDM, enriched in tyrosine, phenylalanine, and alanine (p=8×10⁻⁴, 0.01, 0.01 respectively). The combined machine learning model (microbiome + cytokines + clinical data + diet) achieved auROC=0.83. Microbiome-only models achieved auROC=0.73 in the primary cohort and auROC=0.60 when applied to the independent Chinese cohort.

**Clinical Implications:** This study provides strong evidence that GDM pathogenesis begins in the first trimester, driven at least in part by gut microbiota-induced inflammation, with IL-6 as a key mediator. The ability to predict GDM with high accuracy (auROC=0.83) using first-trimester biomarkers opens a window for early intervention—dietary, lifestyle, or potentially microbiome-targeted—that could prevent GDM onset and its associated maternal and neonatal complications. The finding that microbiome signatures partially generalize across geographically distinct populations (Israeli and Chinese) suggests broad applicability, though local calibration would improve accuracy. Limitations include the observational nature of the human data (causality inferred from mouse FMT experiments), potential confounding by unmeasured variables, and the possibility that non-bacterial fecal components contributed to phenotype transfer.

PICO

PPOPULATION

394 pregnant women aged 18–40 years recruited at gestational weeks 11+0–13+6 from women's health centers in Israel (primary cohort), plus a secondary cohort of 66 women with GDM recruited at 24–28 weeks

IINTERVENTION

Observational — no intervention; profiling of gut microbiome (16S rRNA sequencing), serum cytokines, fecal metabolomics/SCFAs, and clinical data in first trimester

OOUTCOME

GDM diagnosis by glucose tolerance test in second trimester; glucose tolerance and cytokine levels in germ-free mice after fecal microbiota transplant

STUDY TYPE

cohort

SPECIALTY

endocrinology

SUMMARISED BY

AI pipeline

FIDELITY CHECK

100% · A

ORIGINAL PAPER

PMC10086485

Gestational diabetes is driven by microbiota-induced inflammation months before diagnosis

Gut

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ABSTRACT

Objective Gestational diabetes mellitus (GDM) is a condition in which women without diabetes are diagnosed with glucose intolerance during pregnancy, typically in the second or third trimester. Early diagnosis, along with a better understanding of its pathophysiology during the first trimester of pregnancy, may be effective in reducing incidence and associated short-term and long-term morbidities. Design We comprehensively profiled the gut microbiome, metabolome, inflammatory cytokines, nutrition and clinical records of 394 women during the first trimester of pregnancy, before GDM diagnosis. We then built a model that can predict GDM onset weeks before it is typically diagnosed. Further, we demonstrated the role of the microbiome in disease using faecal microbiota transplant (FMT) of first trimester samples from pregnant women across three unique cohorts. Results We found elevated levels of proinflammatory cytokines in women who later developed GDM, decreased faecal short-chain fatty acids and altered microbiome. We next confirmed that differences in GDM-associated microbial composition during the first trimester drove inflammation and insulin resistance more than 10 weeks prior to GDM diagnosis using FMT experiments. Following these observations, we used a machine learning approach to predict GDM based on first trimester clinical, microbial and inflammatory markers with high accuracy. Conclusion GDM onset can be identified in the first trimester of pregnancy, earlier than currently accepted. Furthermore, the gut microbiome appears to play a role in inflammation-induced GDM pathogenesis, with interleukin-6 as a potential contributor to pathogenesis. Potential GDM markers, including microbiota, can serve as targets for early diagnostics and therapeutic intervention leading to prevention.

CITATION EXPORT

Yishay Pinto, Sigal Frishman, Sondra Turjeman et al.. Gestational diabetes is driven by microbiota-induced inflammation months before diagnosis. Gut. (2023). https://doi.org/10.1136/gutjnl-2022-328406

AUTHORS

33 · 19 CENTRES

Yishay Pinto, Sigal Frishman, Sondra Turjeman, Adi Eshel, Meital Nuriel-Ohayon, Oshrit Shrossel, Oren Ziv, William Walters, Julie Parsonnet, Catherine Ley, Elizabeth L Johnson, Krithika Kumar, Ron Schweitzer, Soliman Khatib, Faiga Magzal, Efrat Muller, Snait Tamir, Kinneret Tenenbaum-Gavish, Samuli Rautava, Seppo Salminen, Erika Isolauri, Or Yariv, Yoav Peled, Eran Poran, Joseph Pardo, Rony Chen, Moshe Hod, Elhanan Borenstein, Ruth E Ley, Betty Schwartz, Yoram Louzoun, Eran Hadar, Omry Koren

1 Azrieli Faculty of Medicine, Bar-Ilan University, Safed, Israel
2 Helen Schneider Hospital for Women, Rabin Medical Center and Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
3 Institute of Biochemistry, School of Nutritional Sciences Food Science and Nutrition, The School of Nutritional Sciences, The Robert H. Smith Faculty of Agriculture, Food and Environment, The Hebrew University of Jerusalem, Rehovot, Israel
4 Department of Mathematics, Bar-Ilan University, Ramat Gan, Israel
5 Department of Microbiome Science, Max Planck Institute for Developmental Biology, Tubingen, Germany
6 Department of Medicine, Stanford University, Stanford, California, USA
7 Department of Epidemiology and Population Health, Stanford University, Stanford, California, USA
8 Division of Nutritional Sciences, Cornell University, Ithaca, New York, USA
9 Department of Natural Compounds and Analytical Chemistry, Migal-Galilee Research Institute, Kiryat Shmona, Israel
10 Analytical Chemistry Laboratory, Tel-Hai College, Upper Galilee, Israel
11 Laboratory of Human Health and Nutrition Sciences, Migal-Galilee Technology Center, Kiryat Shmona, Israel
12 Nutritional Science Department, Tel Hai College, Upper Galilee, Israel
13 The Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel
14 Department of Pediatrics, University of Turku and Turku University Hospital, Turku, Finland
15 University of Helsinki & Helsinki University Hospital, New Children’s Hospital, Pediatric Research Center, Helsinki, Finland
16 Functional Foods Forum, University of Turku, Turku, Finland
17 Clalit Health Services, Tel Aviv, Israel
18 Department of Clinical Microbiology and Immunology, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
19 Santa Fe Institute, Santa Fe, New Mexico, USA