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.

Undernutrition affects about one out of five children worldwide. It is associated with impaired growth, neurodevelopment deficits, and increased infectious morbidity and mortality. Undernutrition, however, cannot be solely attributed to a lack of food or nutrient deficiency but rather results from a complex mix of biological and environmental factors. Recent research has shown that the gut microbiome is intimately involved in the metabolism of dietary components, in growth, in the training of the immune system, and in healthy development. In this review, we look at these features in the first three years of life, which is a critical window for both microbiome establishment and maturation and child development. We also discuss the potential of the microbiome in undernutrition interventions, which could increase efficacy and improve child health outcomes.

Microbiome research has expanded significantly in the last two decades, yet translating findings into clinical applications remains challenging. This perspective discusses the persistent issue of correlational studies in microbiome research and proposes an iterative method leveraging in silico, in vitro, ex vivo, and in vivo studies toward successful preclinical and clinical trials. The evolution of research methodologies, including the shift from small cohort studies to large-scale, multi-cohort, and even "meta-cohort" analyses, has been facilitated by advancements in sequencing technologies, providing researchers with tools to examine multiple health phenotypes within a single study. The integration of multi-omics approaches-such as metagenomics, metatranscriptomics, metaproteomics, and metabolomics-provides a comprehensive understanding of host-microbe interactions and serves as a robust hypothesis generator for downstream in vitro and in vivo research. These hypotheses must then be rigorously tested, first with proof-of-concept experiments to clarify the causative effects of the microbiota, and then with the goal of deep mechanistic understanding. Only following these two phases can preclinical studies be conducted with the goal of translation into the clinic. We highlight the importance of combining traditional microbiological techniques with big-data approaches, underscoring the necessity of iterative experiments in diverse model systems to enhance the translational potential of microbiome research.